METHOD AND APPARATUS FOR PRIVACY PROTECTED CLUSTERING OF USER
INTEREST PROFILES
FIELD OF INVENTION
[0001] The present subject matter relates to personalized services and data analytics and,
particularly but not exclusively, to privacy protection of end users while clustering the user
interest profiles for the personalized services and data analytics.
BACKGROUND
[0002] Owing to the huge mass of content available over the World Wide Web, end users
accessing content provided by service providers are often provided personalized assistance and
data analytics by the service providers, search engines, web publishers, and advertisers for easily
accessing relevant content. Conventionally, various techniques, such as content based
recommendation, collaborative recommendation, and data analytics, are used to provide
personalized services to the end users. In content based recommendation, the end users are
recommended content, services or products which are similar to the content, services or products
used or liked by the end users in the past or which match the interest or choice of the end user. In
collaborative recommendation, the end user is recommended content, services or products which
are similar to the content, services or products used or liked by other end users having similar or
same interest or choices.
[0003] In an example of content based recommendation, a movie review website may
monitor an end user to regularly view a certain category of movies, for example, animated
movies. Accordingly, every time an animated movie is available for view, the end user may be
provided a recommendation, such as a notification or an alert, for example, to download the
movie by making relevant payments. Similarly, in collaborative recommendation, also known as
collaborative filtering, service providers may provide targeted advertisements to an end user
where these advertisements pertain to product or services that have been preferred by other end
users who have similar interests and preferences as the end user. For example, an internet
protocol television (IPTV) service provider may recommend television shows or movies to the
end user, if the television shows or movies have been viewed by other end users whose interests
match the interests of the end user.
[0004] Further, users need to provide their personnel data to service providers for
executing data analytics applications, for example, for data mining purposes. In midst of all these
techniques for data analytics and offering relevant content to the end users by the service
providers, users of today feel increasingly concerned about their personal and potentially
sensitive information. This is mainly because in order to benefit from the personalized services
and facilitate data analytics, the end users have to reveal sensitive information, but at the same
time, they are concerned about the privacy protection of the information. For example, an end
user of a social networking site may not object to the use of the information of his accessing
social networking sites to make anonymous recommendations to other end users and to himself
regarding updates on social networking sites as such, but the end user may not want the other
entities, such as other end users, the service providers, attackers and malicious parties, to know
the particular URLs that the end user visited or rated.
SUMMARY
This summary is provided to introduce concepts related to privacy protection while
clustering end users for providing personalized services and executing data analytics
applications. This summary is not intended to identify essential features of the claimed subject
matter nor is it intended for use in determining or limiting the scope of the claimed subject
matter.
[0005] In one implementation, a method for privacy protected clustering of user interest
profiles is described. The method includes generating at least one interest profile segment based
on an interest profile of an end user. The method further includes obtaining semantic terms
corresponding to the at least one interest profile segment based on interaction with a semantic
metadata database coupled to a user device of the end user where the semantic terms are obtained
from amongst one or more semantic terms provided in the semantic metadata database. Each of
the at least one interest profile segment are subsequently transformed into at least one semantic
representation. The method further includes assigning a cluster identifier to the at least one
interest profile segment based on the at least one semantic representation where the cluster
identifiers are generated using locality sensitive hashing (LSH) technique.
[0006] In another implementation, a user device for privacy protected clustering of user
interest profiles is described. In said implementation, the user device includes a processor and a
memory coupled to the processor. The memory includes a profile transformation module
configured to obtain semantic terms corresponding to at least one interest profile segment based
on interaction with a semantic metadata database coupled to the user device. In one embodiment,
the semantic terms are obtained from amongst one or more semantic terms provided in the
semantic metadata database. The profile transformation module is further configured to
transform each of the at least one interest profile segment into at least one semantic
representation. The memory further includes a cluster identifier module configured to generate,
based on the at least one semantic representation, a cluster identifier using locality sensitive
hashing (LSH) and assign the to the cluster identifier to at least one interest profile segment
based on the at least one semantic representation.
[0007] In another implementation, a computer-readable medium having embodied
thereon a computer readable program code for executing a method is described. The method may
include generating at least one interest profile segment based on an interest profile of an end
user. The method further includes obtaining semantic terms corresponding to the at least one
interest profile segment based on interaction with a semantic metadata database coupled to a user
device of the end user where the semantic terms are obtained from amongst one or more
semantic terms provided in the semantic metadata database. Each of the at least one interest
profile segment are subsequently transformed into at least one semantic representation. The
method further includes assigning a cluster identifier to the at least one interest profile segment
based on the at least one semantic representation where the cluster identifiers are generated using
locality sensitive hashing (LSH) technique.
BRIEF DESCRIPTION OF THE FIGURES
[0008] The detailed description is described with reference to the accompanying figures.
In the figures, the left-most digit(s) of a reference number identifies the figure in which the
reference number first appears. The same numbers are used throughout the figures to reference
like features and components. Some embodiments of system and/or methods in accordance with
embodiments of the present subject matter are now described, by way of example only, and with
reference to the accompanying figures, in which:
[0009] Figure 1 illustrates an exemplary network environment implementation of privacy
protected clustering of user profiles for privacy protectior of personal information, according to
an embodiment of the present subject matter;
[0010] Figure 2 illustrates components o f a user device implementing the privacy
protected clustering of user profiles for privacy protection offpersonal information, in accordance
with an embodiment of the present subject matter;
[0011] Figures 3(a), 3(b), and 3(c) illustrate exemplary network environment
implementations of various applications implementing p ri c y protected clustering of user
profiles, according to an embodiment of the present subject t te r; and
[0012] Figure 4 illustrates a method of privacy projected clustering of user profiles to
provide privacy protection to end users, in accordance with embodiment of the present subject
matter.
[0013] In the present document, the word "exemplary" i s used herein to mean "serving as
an example, instance, or illustration." Any embodiment or implementation of the present subject
matter described herein as "exemplary" is not necessarily t o be construed as preferred or
advantageous over other embodiments.
[0014] It should be appreciated by those skilled in t e art that any block diagrams herein
represent conceptual views of illustrative systems embo ing the principles of the present
subject matter. Similarly, it will be appreciated that any flew charts, flow diagrams, state
transition diagrams, pseudo code, and the like represent various processes which may be
substantially represented in computer readable medium a d so executed by a computer or
processor, whether or not such computer or processor is explicitly shown.
DESCRIPTION OFEMBODIMENTS
[0015] Systems and methods for providing privacy to personal information of end users
are described. The systems and methods can be implemented in various user devices used by end
users to avail services or view content provided by a service: provider. Further, the user devices
may be communicating with distributed servers and other sef devices through various networks.
The user devices and distributed servers that can implement t e described method(s) include, but
are not limited to, computing devices, such as a laptop, a desktop computer, a notebook, a mobile
phone, a personal digital assistant, a workstation, a mainframe computer, a set top box, a media
player, central directory servers, database server, file server, print server, web server, application
server, and the like. Although the description herein is with reference to personal computers and
distributed servers providing recommendation services and executing data analytics applications,
the methods and systems may be implemented in other server and computing systems providing
connectivity to the end users for protecting their privacy, as will be understood by a person
skilled in the art.
[0016] Although recommendation services and personalized content are provided to
different end users by service providers through various networks, the methods and systems
described herein are access independent, and support multiple access types including Global
System for Mobile (GSM), Wideband Code Division Multiple Access (W-CDMA), Code
Division Multiple Access (CDMA), Wireless Local Area Network (WLAN), Wireline, Local
Area Network (LAN), and other networks providing connectivity.
[0017] Conventionally, many service providers attempt to personalize the services
provided to end users, such as services of providing content, such as videos, audio, news,
advertisements, etc., based on personal details, preferences, and choices of the end users. For this
purpose, the service providers use various techniques to recommend services, contents, or
products and execute data analytic applications based on factors like personal information, such
as age, sex, and location of the end user; on the past actions of the end user, or past preferences
by other end users who have been identified to have similar interests as that of the end user.
[0018] For example, if an end user, say user A, has purchased a book written by a
particular author, the service provider may suggest the user A to purchase other books written by
the same author or other books on the same or related subjects, etc. In a similar but another
conventional approach, the service provider may recommend to the end user A those contents, or
products that have been preferred by other end users having an interest profile similar to that of
the end user A. For the purpose of providing personalized services and for data analytics, various
conventionally known methods are used to cluster the end users having similar interests into one
or more interest groups. .
[0019] The conventional methods of clustering implemented by the service provider
require collection of information related to personal preferences, choices, etc., of the end users.
Conventionally, the service providers monitor and collect information pertaining to the end users
through various means, such as by analyzing log files, application history files or other
personally identifiable information saved on the end user's client device or provided by the user
while registering with the service provider. In another conventional technique, the service
provider may save a text file, such as a Hyper Text Transfer Protocol (http) cookie to collect
information pertaining to an end user. For example, a web portal may save user preference data
including an http cookie of a web browser of an end user to store the preferences of the end user,
such as font size, arrangement of display widgets, etc. Further, the user preference data may also
store the browsing details of the end user and send the same to the web portal.
[0020] Thus, in an attempt to provide recommendation services, personalized content,
services or products to the end user based on the end user's personal choice, the service provider
often monitors and collects information pertaining to activities of the end user. In certain
situations, it becomes possible to identify the end user on the basis of the information collected
by the service providers. This may result in compromising the personal or confidential
information of the end user and exposes the end user to potential privacy breaches or makes him
the target of advertisers or spammers, etc. Further, in extreme cases, the end user may become a
victim of various crimes, such as identity theft and credit card frauds.
[0021] Further, in present days when end users are connected to each other through
virtual social networks via high speed communication support, end users are constantly sharing
private and personal information to a broad class of applications, such as StumbleUpon ,
GoogleNews™, Foursquare®, Facebook®, Yahoo/®, and twitter™. Although the nature of
sensitive personal information revealed depends on the type of application, such as location for
location based services (LBS) and browsing history for uniform resource locator (URL)
recommender systems; infusion of these applications into the all-encompassing framework of
Online Social Networks (OSNs), wherein OSNs are beginning to act as a One-stop-shop' for
offering recommendation services, is putting the personal information of users at higher risks
than ever.
[0022] Recently, different approaches to preserving privacy, while still executing data
analytics applications and providing personalized services, such as recommendation services;
personalized content, personalized search/query, social networks, data mining, and other related
services, have been proposed.
[0023] One of such existing approach involves clustering the interest profiles using
homomorphic encryption based strategies. In such an approach, the interest profiles of the users
are encrypted using crypto techniques, such as homomorphic encryption in order to provide
privacy to the users. Such crypto techniques enable execution of protocol primitive operations,
such as addition and multiplication for clustering the interest profiles using a distributed
computation setting. Using the distributed computation setting allows executing multiple phases
of the protocol primitive operations over user devices of a static set of end users. For instance,
KMeans clustering algorithm may be implemented in privacy preserving method used the
technique of homomorphic encryption. Although techniques using the homomorphic encryption
facilitate providing privacy to the end users, such techniques are usually resource intensive and
complex to execute. Further, executing multiple phases of the protocol primitive operations over
user devices of a static set of end users restricts application of such techniques in dynamic
environments, such as online portals where the set of end users changes dynamically with end
users constantly joining and leaving the online portals.
[0024] Further, certain approaches provide centralized solution where centralized
intermediaries configured to cluster the interest profiles are un-trusted by the end users. In such
situations, each end user initially pre-process the interest profile using a random projection based
approach, such as encryption to obtain a projected interest profile before being communicated to
the centralized intermediary. The projected interest profile are typically obtained by transforming
the interest profiles using random projection functions not available with the centralized
intermediaries, thus ensuring privacy of the end user's data. The random projection functions are
however, shared among the end user's intending to be a part of a common cluster to ensure that
all the similar interest profiles are transformed into similar projected interest profile for
facilitating an efficient and correct clustering. However, sharing the random projection functions
among the end users may enable leaking of the random projection function as a malicious end
user may provide the random projection function to the centralized intermediaries for obtaining
original interest profiles from the projected interest profiles.
[0025] Another technique for protecting private information is the use of a gossip-onbehalf
approach with a proxy node configured to transmit the interest profile on behalf of the end
user. Such a technique involves transmitting the user profile of the end users to the proxy nodes,
through an intermediary node, using an encrypted two-hop communication in order to hide the
end users identity from the proxy node. However, sharing the complete interest profile with the
proxy node may facilitate linkability attacks as the proxy nodes may identify the end users by
analyzing the interest profiles over a period of time.
[0026] According to an implementation of the present subject matter, systems and
methods for ensuring privacy while clustering user interest profiles of end users for providing
privacy to personal information of the end users are described. As described before, the systems
and methods can be implemented in a variety of processing and communicating devices capable
of communicating with a network according to various different standards defined for the
communication. Further, the systems and methods described herein may be connected through
either wired networks or wireless networks provided via different means.
[0027] The systems and methods as described herein, on one hand, provide true
protection to private information of the end users by providing anonymity to each end user and
their private information without limiting the use of applications, and on the other, also provide
the end user with capability of utilizing any computing device including a thin client, such as a
mobile device with limited Computational capability to receive personalized services or to use
any other applications or services requiring the user clustering according to the similarity of their
private data. In one embodiment, a privacy protected network includes a plurality of end users
associated with user devices for communicating with each other or distributed servers, such as
one or more aggregator nodes.
[0028] In accordance with an embodiment of the present subject matter, to provide
personalized content and recommendation services to an end user, clusters of the end users
having similar interest profiles are created. For the purpose, the user devices of each of the end
users initially generate the interest profile of the end user based on profile data, such as the
personal choices and preferences of the end user. For instance, the user device may obtain list of
all URLs visited by the end user, all videos searched for or watched by the end user, music
listened to and searched by the end user, and data related to other similar interest fields explored
by the end users.
[0029] In one embodiment, the interest profiles may be subsequently segmented into
multiple interest profile segments with each segment corresponding to a particular interest field.
Examples of the interest fields include, but are not limited to, contextual preferences, such as
professional interests, leisure preferences, and religious preferences and areas of interest, such as
music, dance, e-shopping, and social networking. The interest profile segments may be
subsequently processed for clustering into various interest groups. In another embodiment, the
interest profile as a whole may be processed for clustering into various interest groups.
[0030] The interest profile segments are subsequently transformed into compact and
semantically dense representations referred to as semantic representations. Semantic
representations of the interest profile segments include, as an example and not a limitation, tag
based representations, ontology concept based representations, and topic based representations.
For example, the interest profile corresponding to a URL of a social networking site "xyz.com"
may be transformed to the semantic representation "social networking site".
[0031] For the purpose, the user device may interact, over an anonymous communication
channel, with a semantic metadata database having a list or a dictionary of semantic terms that
may be used for transforming the interest profile segments into corresponding semantic
representations. In one embodiment, the user device may provide the interest profile segments to
the semantic metadata database which may identify the semantic term corresponding to each of
the interest profile segments to transform the interest profile segments into the corresponding
semantic representations. In another embodiment, the user device may obtain the list of the
semantic terms from the semantic metadata database and then identify the semantic term
matching the interest profile segment to obtain the corresponding semantic representations. In
yet another embodiment, the user device itself may include a metadata having the semantic terms
extracted from web pages browsed by the end user. In said embodiment, the user devices are
configured to share a vocabulary of semantic terms with each other so that the semantic
representations generated by the different user devises are expressed using same terms and are
therefore comparable.
[0032] The semantic representations thus obtained may be assigned a cluster identifier to
identify the interest groups to which the end user can be associated. The cluster identifiers may
be assigned using a technique of locality sensitive hashing (LSH). The LSH technique involves
converting each of the semantic representations into corresponding hash codes, i.e., the cluster
identifiers, using the semantic representations and a set of hash functions defined by random
values, such as a common sequence of random vectors generated at each of the user device.
Assigning cluster identifiers at the user device facilitates in providing privacy protection as the
interest profile are not shared with the remote nodes. Further, using the LSH technique helps in
increasing the efficiency in the clustering of the user profiles as the clustering can now be
performed easily by matching the hash codes. Furthermore, using the semantic metadata
database helps in reducing the occurrences of false negatives and false positives, thus increasing
the efficiency of the clustering. Thus, performing the clustering by the LSH technique in
conjunction with profile transformation by generating the semantic representations facilitates in
providing privacy protection with reduced errors in clustering.
[0033] The cluster identifiers thus obtained may be used for clustering the user device
into one or more clusters, i.e., interest groups corresponding to the cluster identifiers. Further, the
cluster identifiers are provided to one or more remote nodes, for example, a central aggregator,
an interest group aggregator, and another user device over a communication channel, for
instance, an anonymous communication channel.
[0034] In one implementation, the remote node communicating with the user device may
be selected depending on the application or type of recommendation based service for which the
user device may be used. For instance, in case of a collaborative filtering application, the remote
nodes may be central aggregators with each central aggregator corresponding to at least one
cluster, i.e., an interest group, such as movies, dances, e-shopping, social networking sites, or a
combination thereof. The user device may thus provide each of the cluster identifiers to the
corresponding central aggregator along with the interest profiles of the user to enable the central
aggregator to identify highest ranked, i.e., top ranked interest field after the interest field of the
interest group in their corresponding interest groups. For example, a central aggregator
responsible for the interest group on 'music' may receive interest profiles of all the user devices
having the corresponding cluster identifier. The central aggregator may subsequently analyze the
user profiles to identify other interest fields or interests ranked highly because of, for example,
being present in interest profiles of a majority, say 90 %, end users. For instance, the central
aggregator may identify 'dance ' as the highest ranked interest and may thus obtain
recommendations corresponding to dance from the central aggregator responsible for the interest
field 'dance '. The central aggregator may thus provide contents and recommendations related to
music as well as dance to all the user devices present in the cluster corresponding to the cluster
'music '. The central aggregator may thus provide contents and recommendations related to not
only the interest of the user but also a new interest or item that is highly ranked in the interest
group. In another embodiment, the central aggregator may be replaced with each user device
acting as a central aggregator responsible for an interest group.
[0035] Similarly, in case of content-based recommendation applications, the remote
nodes may be interest group aggregators with each interest group aggregator corresponding to at
least one cluster, i.e., an interest group, such as movies, dances, e-shopping, social networking
sites. The user devices in such a case are configured to transmit the cluster identifier and the
semantic representations to the interest group aggregator corresponding to the cluster identifier,
based on which the interest group aggregator may provide content corresponding to the cluster
identifier.
[0036] Further, in applications involving peer-to-peer communications, the remote nodes
may be other user devices. In such applications, the user devices may use the cluster identifiers
to identify other user devices having similar cluster identifiers so as to enable chatting and
exchange of data between the user devices using anonymous communication channels. Using the
anonymous communication channels helps in ensuring that the other users or the remote nodes
are not able to identify the end user transmitting the cluster identifiers as the cluster identifiers
and other data are sent using various intermediary nodes forming the anonymous communication
channels.
[0037] The systems and methods of the present subject matter thus facilitate in providing
privacy to the end users by assigning cluster identifiers to the interest profiles at the user devices.
Assigning cluster identifiers to the interest profiles at the user devices using the LSH technique
enables clustering of similar users at remote nodes without the remote nodes accessing the
interest profiles of the end users, thus providing privacy protection to the end users. Further,
using the LSH technique in conjunction with generating the semantic representations helps in
ensuring that privacy is protected and errors in clustering are reduced as using the semantic
metadata database helps in reducing the occurrences of false negatives and false positives, thus
increasing the efficiency in clustering. Thus, providing cluster identifiers at the user devices
facilitates content providers in performing data analytics and providing personalized services
faster and efficiently as the services provides now do not need to analyze the interest profiles and
may use any content matching techniques to match the cluster identifier with the content data.
Additionally, using the cluster identifiers reduces costs and saves resources at the service
provider's end as the service providers now do not need to process or analyze the interest
profiles.
[0038] It should be noted that the description and figures merely illustrate the principles
of the present subject matter. It will thus be appreciated that those skilled in the art will be able to
devise various arrangements that, although not explicitly described or shown herein, embody the
principles of the present subject matter and are included within its spirit and scope. Furthermore,
all examples recited herein are principally intended expressly to be only for pedagogical
purposes to aid the reader in understanding the principles of the present subject matter and the
concepts contributed by the inventor(s) to furthering the art, and are to be construed as being
without limitation to such specifically recited examples and conditions. Moreover, all statements
herein reciting principles, aspects, and embodiments of the present subject matter, as well as
specific examples thereof, are intended to encompass equivalents thereof.
[0039] It will also be appreciated by those skilled in the art that the words during, while,
and when as used herein are not exact terms that mean an action takes place instantly upon an
initiating action but that there may be some small but reasonable delay, such as a propagation
delay, between the initial action and the reaction that is initiated by the initial action.
Additionally, the word "connected" and "coupled" is used throughout for clarity of the
description and can include either a direct connection or an indirect connection.
[0040] The manner in which the systems and methods of clustering user profiles for
privacy protection to end users shall be implemented has been explained in details with respect
to the Figures 1-4. While aspects of described systems and methods for clustering user profiles
for providing privacy protection can be implemented in any number of different computing
systems, transmission environments, and/or configurations, the embodiments are described in the
context of the following exemplary system(s).
[0041] Figure 1 illustrates a network environment 100 implementation of privacy
protected clustering of user profiles for privacy protection of end users while facilitating data
analytics and accessing personalized services, in accordance with an embodiment of the present
subject matter. The network environment 100 includes one or more user devices 102-1, 102-2,
and 102-N, collectively referred to as user devices 102 and individually referred to as user
device 102 hereinafter, communicating with each other and a remote node 104 through a
communication network 106.
[0042] The communication network 106 may be a wireless network, or a combination of
wired and wireless network. The communication network 106 can be a collection of individual
networks, interconnected with each other and functioning as a single large network (e.g., the
internet or an intranet). Examples of such individual networks include, but are not limited to, 3rd
Generation Partnership Project (3GPP), Long Term Evolution (LTE), Global System for Mobile
Communication (GSM) network, Universal Mobile Telecommunications System (UMTS)
network, Personal Communications Service (PCS) network, Time Division Multiple Access
(TDMA) network, Code Division Multiple Access (CDMA) network, Next Generation Network
(NGN), Public Switched Telephone Network (PSTN), and Integrated Services Digital Network
(ISDN). The communication networks 106 may be any public or private network, including a
local area network (LAN), a wide area network (WAN), the Internet, an intranet, a peer to peer
network and a virtual private network (VPN) and may include a variety of network devices, such
as routers, bridges, servers, computing devices, storage devices, etc.
[0043] The user devices 102 may be defined as User Equipments (UEs) used by end
users to communicate with each other or with other network entities such as remote node 104
and distributed servers for facilitating data analytics and accessing various personalized services,
such as recommendation services; personalized content, personalized search/query, social
networks, data mining, and other related services. Examples of the user devices 102 may include,
without limitation, computing devices, such as a laptop or other portable computers, a desktop
computer, a notebook, network computers; a mobile phone; hand-held devices; a personal digital
assistant; a workstation; a mainframe computer; a set top box a media player; central directory
servers; database server; file server; print server; web server; application serve; and the like.
Each of the user devices 102 works on a communication protocol as defined by the
communication network 106 to which the user device 102 is coupled. Further, the user devices
102 are configured to interact with each other or other network entities, such as the remote node
104 over the communication network 106 using one or more of communication channels 108-1,
108-2, 108-3, 108-4, 108-5..., 108-n, hereinafter collectively referred to as the communication
channels 108. In one implementation the communication channels 108 may be anonymous
communication channels configured based on, for example, onion routing framework, such as
TOR to support anonymous communication between the user devices 102 and between the user
devices 102 and other network entities, such as the remote node 104.
[0044] The remote node 104 can be implemented as a variety of computing devices, such
as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a
server, and the like. Although the remote node 104 is shown as an entity, the remote node 104
may also be implemented as a distributed computing system including multiple intermediary
nodes distributed over a network where each node can be implemented as a computing devices,
such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe
computer, a server, and the like. Further, the intermediary nodes may be connected through an
intermediate network (nor shown in the figure) for the purpose of communications and exchange
of data. Further, examples of the remote node 104 include, but are not limited to, a central
aggregator, an interest group aggregator, and a user device 102.
[0045] In one implementation, the remote node 104 communicating with the user device
102 may be selected depending on the application or type of recommendation based service for
which the user device 102 may be used. For instance, in case of a collaborative filtering
application, the remote node 104 may be a central aggregator, similarly in the case of contentbased
recommendation applications, the remote node 104 may be implemented as interest group
aggregators.
[0046] In one embodiment, interest profiles of the end users are generated based on the
activities of the end users and saved locally by the user device 102. For example, the interest
profiles of the end users may be generated based on profile data corresponding to the end users.
The profile data may indicate, for example, websites visited by the end users, songs or videos
played or downloaded by the end users, products used or services availed or reviewed by the end
users, etc. Based on the generated interest profile, the client device categorizes the end user in
one or more pre-defined interest groups. Interest groups may be understood as groups of end
users sharing similar interests and choices.
[0047] For the purpose, the user device 102 may include a profile transformation module
10 and a cluster identifier module 112. The profile transformation module 110 is configured to
transform the interest profile into compact and semantically dense representations, referred to as
semantic representations. Semantic representations include, as an example and not a limitation,
tag based representations, ontology concept based representations, and topic based
representations. In one embodiment, the complete interest profile may be transformed to the
semantic representations by the profile transformation module 110. In another embodiment
segments of the interest profile, hereinafter referred to as interest profile segments, may be
transformed into the semantic representations. Although the description herein is with reference
to interest profile segments, the methods and systems may be implemented for transforming the
complete interest profile, albeit with a few variations, as will be understood by a person skilled in
the art.
[0048] In order to transform the interest profiles, the profile transformation module 1 0
may interact with a database, such as a semantic metadata database 114 over the communication
channel 108-1 to obtain metadata related to each of the interest profile segments. The semantic
metadata database 114 may be understood as a dictionary having a list of semantic terms that
may be used for transforming the interest profile segments into corresponding semantic
representations. The semantic terms may be understood as terms that identify various interest
fields of the end user based on their broad meaning or definition and thus may be used for
efficient and errorless matching of various interest profiles based on concept of each interest
field. Examples of the interest fields include, but are not limited to, contextual preferences, such
as professional interests, leisure preferences, and religious preferences and areas of interest, such
as music, dance, e-shopping, and social networking. For instance, the interest profiles
corresponding to two different URLs of a social networking site "xyz.com" may be matched to
the semantic term "social networking site" in spite of the URLs being of different versions and
thus being syntactically different. Although the semantic metadata database 114 has been shown
external to the user device 102, it will be understood that the semantic metadata database 114
may be internal to the user device 102 in other embodiments. Further, in lateral cases the
semantic metadata databases may be harmonized across the user devices by exchanging
vocabulary and terms between the user devices.
[0049] The semantic representations thus generated by the profile transformation module
110 may be provided to the cluster identifier module 112 for assigning a cluster identifier to each
of the semantic representations. The cluster identifiers may be understood as interest group
identity codes that may be used for efficiently identifying the clusters, i.e., interest groups
corresponding to the cluster identifiers to which the end user can be associated. The cluster
identifier module 112 may be configured to use a technique of LSH for assigning the cluster
identifiers. The LSH technique may be used by the cluster identifier module 112 to convert each
of the semantic representations into corresponding hash codes, i.e., the cluster identifiers. For the
purpose, the cluster identifier module 112 utilizes a set of hash functions defined by random
values, such as a common sequence of random vectors generated at each of the user devices 102.
[0050] In one implementation, the hash functions are obtained by the cluster identifier
module 112 from a central entity, such as a random seed generator (not shown in the figure)
accessible by all the user device 102 to ensure cluster identification is done uniformly by all the
user devices 102. In another implementation, the cluster identifier module 112 may be
configured to generate the hash functions based on seed generation functions obtained from, for
example, the central entity. Assigning cluster identifiers at the user device 102 thus facilitates in
providing privacy protection as the interest profile are not shared with the remote nodes.
Performing the clustering by the LSH technique in conjunction with profile transformation by
generating the semantic representations thus facilitates in providing privacy protection with
reduced errors in clustering.
[0051] The cluster identifiers thus obtained may be used for clustering user profiles of
the user device 102, i.e., the end users into one or more clusters. Further, the cluster identifier
module 112 provides the cluster identifiers to one or more remote nodes 104 over the
communication channel 108 for one or more services, such as clustering for providing
recommendation services or for peer-to-peer communication. Based on the application or type of
recommendation based service which the user device 102 may want to utilize, the remote node
104 communicating with the user device 102 may vary from among a central aggregator and an
interest group aggregator. Alternately, the user devices 102 may communicate with each other
with each user device 102 acting as a remote node for other use devices 102.
[0052] For instance, in case of a collaborative filtering application, the remote nodes may
be central aggregators with each central aggregator corresponding to at least one cluster, i.e., an
interest group, such as movies, dances, e-shopping, social networking sites, and a combination
thereof. The remote node 104 in such a case is configured to provide to each member of the
cluster, recommendations related to not only the interest field corresponding to the group but
also related to other top rated, i.e., highly ranked interest fields of the end users who are member
of the cluster. In another embodiment, the remote node 104 acting as the central aggregator may
be removed with each user device 102 acting as a central aggregator responsible for an interest
group.
[0053] Similarly, in case of content-based recommendation applications, the remote node
104 may be implemented as one or more interest group aggregators with each interest group
aggregator corresponding to at least one cluster, such as movies, dances, e-shopping, social
networking sites. Further, in applications involving peer-to-peer communications, the remote
node 104 may be another other user device. In such applications, the user devices 102 may use
the cluster identifiers to identify other user devices 102 having similar cluster identifiers.
[0054] The systems and methods of the present subject matter thus facilitate in providing
privacy to the end users by assigning cluster identifiers to the interest profiles at the user devices.
[0055] Figure 2 illustrates components of the user device 102 implementing the privacy
protected clustering of user profiles, in accordance with an embodiment of the present subject
matter. In said embodiment, the user device 102 includes one or more processor(s) 202, I/O
interface(s) 204, and a memory 206 coupled to the processor(s) 202. The processor(s) 202 may
be implemented as one or more microprocessors, microcomputers, microcontrollers, digital
signal processors, central processing units, state machines, logic circuitries, and/or any devices
that manipulate signals based on operational instructions. The processor(s) 202 can be a single
processing unit or a number of units, all of which could also include multiple computing units.
Among other capabilities, the processor(s) 202 are configured to fetch and execute computerreadable
instructions stored in the memory 206.
[0056] Functions of the various elements shown in the figure, including any functional
blocks labeled as "processor(s)", may be provided through the use of dedicated hardware as well
as hardware capable of executing software in association with appropriate software. When
provided by a processor, the functions may be provided by a single dedicated processor, by a
single shared processor, or by a plurality of individual processors, some of which may be shared.
Moreover, explicit use of the term "processor" should not be construed to refer exclusively to
hardware capable of executing software, and may implicitly include, without limitation, digital
signal processor (DSP) hardware, network processor, application specific integrated circuit
(ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software,
random access memory (RAM), and non volatile storage. Other hardware, conventional and/or
custom, may also be included.
[0057] The I/O interface(s) 204 may include a variety of software and hardware
interfaces, for example, interface for peripheral device(s), such as a keyboard, a mouse, an
external memory, a printer, etc. Further, the I/O interface(s) 204 may enable the user device 102
to communicate over the communication network 106, and may include one or more ports for
connecting the user device 102 with other computing devices, such as web servers and external
databases. The I O interface(s) 204 may facilitate multiple communications within a wide variety
of protocols and networks, such as a network, including wired networks, e.g., LAN, cable, etc.,
and wireless networks, e.g., WLAN, cellular, satellite, etc.
[0058] The memory 206 may include any computer-readable medium known in the art
including, for example, volatile memory, such as static random access memory (SRAM) and
dynamic random access memory (DRAM), and/or non-volatile memory, such as read only
memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and
magnetic tapes. The memory 206 further includes modules 208 and data 210. The modules 208
include routines, programs, objects, components, data structures, etc., which perform particular
tasks or implement particular abstract data types. The data 210, amongst other things, serves as a
repository for storing data processed, received, and generated by one or more of the modules
208.
[0059] The modules 208 further include an interest profile generation module 212, a
profile segmentation module 214, the profile transformation module 110, the cluster identifier
module 112, a data transfer module 216, and other modules 218. The other module(s) 218 may
include programs or coded instructions that supplement applications and functions of the user
device 102, for example, programs in the operating system.
[0060] The data 210 includes profile data 220, transformed profile data 222, cluster
identifier data 224, and other data 226. The other data 226 may include data generated as a result
of the execution of one or more modules in the other module(s) 218.
[0061] As previously described, the user device 102 is configured to provide privacy
protection to the end user by computing the cluster identifiers for clustering the user profiles. In
one embodiment, the interest profile generation module 212 is configured to generate the interest
profile of the end user of the user device 102 based on the profile data 220. The profile data 220
may indicate, for example, websites visited by the end users, songs or videos played or
downloaded by the end users, products used or services availed or reviewed by the end users, etc.
For instance, the interest profile generation module 212 may obtain details of all videos searched
for or watched by the end user, all URLs visited by the end user, music listened to and searched
by the end user, and similar data related to other interest fields explored by the end users. The
interest profiles thus generated may be stored in the profile data 220. The profile transformation
module 110 may subsequently transform the interest profile into the semantic representations for
assigning cluster identifiers. As discussed previously, the profile transformation module 110 may
be configured to transform either the complete interest profile or the interest profile segments
into the semantic representations. In latter cases, the interest profiles are accessed and segmented
by the profile segmentation module 4 before being transformed by the profile transformation
module 110.
[0062] In one embodiment, the profile transformation module 110 is configured to
segment interest profile into multiple interest profile segments with each interest profile
segments corresponding to a particular interest field. In one implementation, the profile
transformation module 110 may analyze the interest profile of the end user to segment the
interest profile. The profile transformation module 110 may segment the interest profiles into the
interest profile segments based on either contextual preferences, such as leisure preferences and
professional interests or explicit areas of interest, such as music, dance, e-shopping, and social
networking. The interest profile segments thus obtained may be provided to the profile
transformation module 110.
[0063] The profile transformation module 110 transforms the interest profile segments or
the complete interest profiles into corresponding semantic representations based on metadata
provided in the semantic metadata database 1 4 that may be internal or external to the user
device 102. In one embodiment, the profile transformation module 110 may provide the interest
profile segments to the semantic metadata database 114. The semantic metadata database 114 in
such a case may identify the semantic terms corresponding to each of the interest profile
segments and provide the same to the profile transformation module 110 for transforming the
interest profile segments into the corresponding semantic representations. In another
embodiment, the profile transformation module 110 may obtain the list of the semantic terms
from the semantic metadata database 114. The profile transformation module 110 in such a case
may be configured to identify the semantic term, from the list of the semantic terms, matching
the interest profile segment to obtain the corresponding semantic representations.
[0064] In yet another embodiment, the profile transformation module 110 may generate
the semantic representations by tagging the interest profile segments or the complete interest
profiles using metadata, such as tags extracted from web pages visited by the end user on the
user device 102. Such locally extracted tags may be stored as internal semantic metadata in the
other data 226 of the user device 102. In said embodiment, the user devices are configured to
share a vocabulary of semantic terms with each other so that the semantic representations
generated by the different user devises are expressed using same terms and are therefore
comparable. Using the semantic metadata database helps in reducing the occurrences of false
negatives and false positives, thus increasing the efficiency of the clustering, The semantic
representations thus obtained may be stored in the transformed profile data 222 and accessed by
the cluster identifier module 112 for assigning the cluster identifiers to each of the semantic
representations for further clustering.
[0065] The cluster identifier module 112, as described in figure 1, is configured to assign
cluster identifiers to each of the semantic representations to enable clustering of the end user's
profile. The cluster identifier module 112 uses the LSH technique for assigning the cluster
identifiers to the semantic representations. As will be understood by a person skilled in the art, in
LSH technique, two similar objects hash to the same value with a high probability as a hash
collision probability of two profiles is proportional to similarity between the two profiles, as
represented using the following equation:
Pr[h(x) = h(y)] sim(x,y) (1)
where x and y are two profiles, h() is a LSH function, and sim() is a similarity measure between
to profiles. Further, to achieve the functionality of the LSH technique, it is ensured that similar
hash functions are used by the user device 102 for the LSH technique.
[0066] The cluster identifier module 112 is configured to use the value generated by the
hash functions as the cluster identifier, i.e., the label or the group id of the interest group of the
end users having similar interests, i.e., the end users having similar interest profiles. For the
purpose, the cluster identifier module 112 initially obtains a set of random vectors, also referred
to as hash functions, from a central entity, such as the random seed generator. The cluster
identifier module 112 subsequently uses, for example, cosine similarity method of the LSH
technique. In said method the cluster identifier module 112 obtains dot product between the
semantic representations and each of the random vectors and concatenates sign of the dot
products to obtain the hash code as the cluster identifier for the semantic representation. The
cluster identifiers thus obtained may be stored in the cluster identifier data 224. Although the
description herein is with reference to cosine similarity method of the LSH technique, the
methods and systems may be implemented using other implementations of the LSH technique, as
will be understood by a person skilled in the art. Performing the clustering by the LSH technique
in conjunction with profile transformation by generating the semantic representations facilitates
in providing privacy protection with reduced errors in clustering. Further, generating the
semantic representations before performing the clustering using the LSH technique helps in
ensuring that the remote node 104 is not able to perform a linear search over the cluster
members.
[0067] The data transfer module 216 subsequently transmits the cluster identifiers to the
remote node 104 over the communication channels 108, for example, for receiving the
recommendation or contents from the service provider. In one implementation, the method of
transferring the cluster identifiers to the remote node 104 may vary depending on the application
for which the clustering is performed. For instance, in case of the collaborative filtering
application, the data transfer module 216 may be configured to transmit not only the cluster
identifiers but also the interest profile of the end user to the remote node 104 to enable the
remote node to analyze the user profile for providing group recommendations. The data transfer
module 216 in such a case may be configured to slice the interest profiles into smaller profile
slices and transmit each of the smaller profile slices to the remote node 104 independently over
different anonymous communication channel, such as the communication channels 108. The
profile slices, unlike the profile segments, are transmitted without being converted to semantic
representations so as to enable the remote node to analyze the profile slices. Transmitting the
user profiles as different slices over anonymous communication channels ensures privacy
protection as the remote node 104 may not be able to easily identify the end users based on the
slices of the interest profile. In another embodiment of the collaborative filtering application, the
remote node 104 may be replaced and each user device (102) may be configured to act as a
central aggregator responsible for an interest group.
[0068] Further, in case of the content-based recommendation applications, the data
transfer module 216 may be configured to transmit the cluster identifier and the semantic
representations to the remote node 104 for utilizing the recommendation or analytics services
offered by the service providers. Similarly in the case of peer-to-peer communications, the data
transfer module 216 may be configured to identify other user devices 102 having similar cluster
identifiers so as to enable chatting and exchange of data between the user devices 102. On
identifying the other user devices 102, the data transfer module 216 may be configured to
transmit establish anonymous messaging channels and anonymous chat or search broadcast
channels between the other user devices. The working of the data transfer module 216 in each of
the applications will be explained in greater details while describing the applications in the
figures 3(a), 3(b), and 3(c).
[0069] Figures 3(a), 3(b), and 3(c) illustrate exemplary network environment
implementations 300, 302, and 304, respectively, of privacy protected clustering of user profiles
for various applications, according to an embodiment of the present subject matter.
[0070] Figure 3(a) illustrates the network environment 300 implementing the privacy
protected clustering of user profiles for collaborative filtering application providing group
recommendations to members of a cluster. In said embodiment of a collaborative filtering
application, the remote node 104 is implemented as the central aggregator corresponding to at
least one cluster, such as movies, dances, e-shopping, social networking sites. The user device
102 may thus provide each of the cluster identifiers to the corresponding central aggregator along
with the interest profiles of the end user to enable the central aggregator to identify highest
ranked, i.e., top ranked interest field after the interest field of the interest group in their
corresponding interest groups.
[0071] In one embodiment, the data transfer module 216 is configured to slice the interest
profile into smaller independent slices that may be communicated through various
communication channels 108 in order to ensure unlinkability between the various profile
segments received by the remote node 104. Providing the unlinkability ensures that the remote
node 104 may not be able to identify whether the different profile segments belong to the same
user or a different user. The data transfer module 216 ensures that no slice of the interest profile of
the end user by itself contains enough profile information that can be used to construct the
complete interest prolife and infer the identity of the end user. In another embodiment, the whole
interest profile may be sent as a single slice to the remote node 104.
[0072] Further, each of the interest profile slices and the cluster identifier are sent by the
data transfer module 216 through anonymous communication channels over a network
employing mechanisms which ensures anonymity, for example, a network implementing onion
routing. In one implementation, an onion-routing path is established wherein the data transfer
module 216 encrypts the slices of the profile information and the cluster identifier pertaining to
the end user with the public-key of an exit node of the onion-routing path. The various slices of
the profile information and the cluster identifiers pertaining to the end user are transmitted over
one or more intermediate nodes before reaching the exit node. The exit-node decrypts the
information and transmits the same to the remote node 104.
[0073] Further, in order to ensure user privacy, the data transfer module 216 may be
configured to remove, from the interest profile, all data having personally identifiable
information, such as URLs that may reveal the identity of the end users to the remote node 104.
[0074] On receiving the interest profile and the cluster identifiers of the end user, the
remote node 104 analyzes the interest profile to determine the highest or one or more highly
ranked interest of the end users clustered in the cluster corresponding to the remote node 104.
For example, the remote node 104 responsible for the interest group 'social networking' may
receive interest profiles of all the user devices having the cluster identifier corresponding to the
cluster 'social networking '. The remote node 104 may subsequently analyze the user profiles of
the user device 102 to identify other interests ranked highly. On identifying, for instance, 'news'
as the highest ranked interest the remote node 104 may obtain recommendations corresponding
to 'news' from the central aggregator responsible for the interest field 'news'. The remote node
104 may thus provide contents and recommendations related to social networking as well as
news to all the user devices 102 present in the cluster corresponding to the cluster 'social
networking '.
[0075] In another embodiment of the collaborative filtering application, the remote node
104 acting as the central aggregator may be removed with each user device 102 acting as a
central aggregator responsible for an interest group. For the purpose, the data transfer module
216 is configured to maintain at least a partial image of the central aggregator node of the
interest groups of the end user. In said embodiment, the end users having similar interest profiles
may be identified based on the conventional gossip based random peering protocol, as will be
known to persons skilled in the art. The data transfer module 216 may thus be configured to
transmit and receive the cluster identifiers of all the user devices 102 to identify the end users
having similar cluster identifier, i.e., similar interest groups.
[0076] Figure 3(b) illustrates the network environment 302 implementing the privacy
protected clustering of user profiles for content-based recommendation applications. In said
embodiment the remote node 104 is implemented as one or more interest group aggregators with
each interest group aggregator corresponding to at least one cluster, such as movies, dances, eshopping,
social networking sites.
[0077] The data transfer module 216, in such a case, is configured to transmit the cluster
identifier and the semantic representations to the remote node 104 over an anonymous
communication channel, such as the communication channel 108. For the purpose, the data
transfer module 216 establishes an onion-routing path with the data transfer module 216
encrypting the cluster identifier pertaining to the end user with the public-key of an exit node of
the onion-routing path as described in figure 3(a). The remote node 104 in such a case is
configured to select recommendations or contents related to the interest field represented by the
cluster identifier by matching the cluster identifier with a list of interest fields using any of the
well known content matching techniques. For example, if the end user, say user A, belongs to a
cluster having interest field 'books ', the service provider may provide a list of latest top sellers to
the user A.
[0078] Figure 3(c) illustrates the network environment 304 implementing the privacy
protected clustering of user profiles for applications related to peer-to-peer communications. In
said embodiment the remote node 104 is removed as the user devices 102 use the clustering for
interacting with each other in a privacy protected environment. In such applications, the user
devices 102 may use the cluster identifiers to identify other user devices 102 having similar
cluster identifiers. The data transfer module 216, in such a case, is configured to transmit the
cluster identifier over an anonymous communication channel, such as the communication
channel 108 to the other user devices 102. The data transfer module 216 use a gossip based
random peering protocol to transmit the cluster identifier and identify user device 102 having
similar cluster identifiers. The user devices 102 having common cluster identifiers may
subsequently connect to each other anonymously using, for example, onion routing framework,
such as TOR so as to enable chatting and exchange of data between the user devices without
revealing their identity or personnel information.
[0079] Figure 4 illustrates a method of privacy protected clustering of user profiles to
provide privacy protection to end users, in accordance with an embodiment of the present subject
matter. The order in which the method is described is not intended to be construed as a
limitation, and any number of the described method blocks can be combined in any order to
implement the method 400 or any alternative methods. Additionally, individual blocks may be
deleted from the methods without departing from the spirit and scope of the subject matter
described herein. Furthermore, the methods can be implemented in any suitable hardware,
software, firmware, or combination thereof.
[0080] The method(s) may be described in the general context of computer executable
instructions. Generally, computer executable instructions can include routines, programs, objects,
components, data structures, procedures, modules, functions, etc., that perform particular
functions or implement particular abstract data types. The method may also be practiced in a
distributed computing environment where functions are performed by remote processing devices
that are linked through a communications network. In a distributed computing environment,
computer executable instructions may be located in both local and remote computer storage
media, including memory storage devices.
[0081] A person skilled in the art will readily recognize that steps of the methods can be
performed by programmed computers. Herein, some embodiments are also intended to cover
program storage devices, for example, digital data storage media, which are machine or
computer readable and encode machine-executable or computer-executable programs of
instructions, where said instructions perform some or all of the steps of the described method.
The program storage devices may be, for example, digital memories, magnetic storage media,
such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data
storage media. The embodiments are also intended to cover both communication network and
communication devices configured to perform said steps of the exemplary methods.
[0082] At block 402, profile data corresponding to an end user is obtained by a user
device, for example, the user device 102. In one implementation, the profile data may be
obtained based on websites visited by the end users, songs or videos played or downloaded by
the end users, products used or services availed or reviewed by the end users, etc. An interest
profile generation module, for example, the interest profile generation module 212 may be
configured to obtain details of all videos searched for or watched by the end user, all URLs
visited by the end user, all queries made by the end user, and similar data related to other interest
fields explored by the end users. The interest profiles thus generated may be stored as the profile
data.
[0083] At block 404, the profile data is segmented into one or more interest profile
segments. In one implementation, a profile segmentation module, such as the profile
segmentation module 214 is configured to segment the interest profile into multiple segments,
refereed to as the interest profile segments. The interest profile segments may be understood as
independent segments of the interest profiles with each segment corresponding to a particular
interest field. Examples of the interest fields include, but are not limited to, contextual
preferences, such as professional interests, leisure preferences, and religious preferences and
areas of interest, such as music, dance, e-shopping, and social networking. Each of the interest
profile segments are subsequently processed independently to obtain the cluster identifiers for
clustering. Further, in another embodiment, the interest profile as a whole may be provided as a
single interest profile segment for the clustering.
[0084] At block 406, semantic terms corresponding to each of the interest profile
segments are ascertained based on interaction with a semantic metadata database. In one
implementation, a profile transformation module, such as the profile transformation module 110
may be configured to provide the interest profile segments to the semantic metadata database
over an anonymous communication channel. On receiving interest profile segments, the semantic
metadata database identifies, from a list of semantic terms, the semantic terms corresponding to
each of the interest profile segments. Alternately, the profile transformation module 110 may
interact with an internal semantic metadata of the user device 102 populated using data, such as
tags extracted from web pages visited by the end user on the user device 102. In said
embodiment, the user devices 102 are configured to share a vocabulary of semantic terms with
each other so as to maintain uniformity across semantic terms generated used by all the user
devices 102.
[0085] At block 408, interest profile segments are transformed to generate corresponding
semantic representations based on the semantic terms. For example, a profile transformation
module, such as the profile transformation module 110 is configured to transform the interest
profile segments, for example, by tagging each of the interest profile segments with
corresponding semantic representations obtained from the semantic metadata database. The
interest profile segments may be alternately transformed into ontology concept based or topic
based semantic representations. The semantic representations thus obtained may be saved in the
transformed profile data 222.
[0086] At block 410, cluster identifiers are assigned to the semantic representations using
LSH technique. In one implementation, hash functions defined by a set of random values, such
as a common sequence of random vectors are used for assigning the cluster identifiers. For
example, a cluster identification module, such as the cluster identifier module 112 may obtain the
hash functions from a central entity, such as a random seed generator accessible by the user
devices to ensure uniform cluster identification. Performing the clustering by the LSH technique
in conjunction with profile transformation using a common set of semantic representations thus
facilitates in providing privacy protection with reduced errors in clustering.
[0087] At block 412, cluster identifiers corresponding to each of the interest profile
segments are provided to a remote node for clustering the user interest profiles based on the
cluster identifiers. In one implementation, the remote node may be a central entity and may vary
from a central aggregator node to an interest aggregator node depending on the type of
application for which the clustering is being performed. For instance, the remote node may be
implemented as an interest group aggregator in case of content-based recommendation
applications. The user devices in such a case are configured to transmit, over an anonymous
communication channel, each of the cluster identifiers and the semantic representations to the
interest group aggregator corresponding to the cluster identifiers.
[0088] Similarly, in case of a collaborative filtering application, the remote nodes may be
implemented as central aggregators configured to provide to each member of the cluster,
recommendations related to not only the interest field corresponding to the group but also related
to other highly ranked interest fields of the end users of the cluster. The user devices iri such a
case are configured to transmit, over an anonymous communication channel, each of the cluster
identifiers along with slices of the interest profiles of the end user to enable the user devices to
the interest group aggregator corresponding to the cluster identifiers. In another embodiment, the
user devices may interact with other user devices such that each of the user devices acts either as
a remote node for the other user devices or a central aggregator responsible for an interest group.
[0089] Although embodiments for methods and systems for privacy protected clustering
of user interest profiles in a communication network have been described in a language specific
to structural features and/or methods, it is to be understood that the invention is not necessarily
limited to the specific features or methods described. Rather, the specific features and methods
are disclosed as exemplary embodiments for providing privacy protected clustering of user
interest profiles.
I/We claim:
1. A method for privacy protected clustering of interest profiles of end users, the method
comprising:
generating at least one interest profile segment based on an interest profile of an end user;
obtaining semantic terms corresponding to the at least one interest profile segment based
on interaction with a semantic metadata database communicatively coupled to a user device of
the end user, wherein the semantic terms are obtained from amongst one or more semantic terms
provided in the semantic metadata database;
transforming each of the at least one interest profile segment into at least one semantic
representation based on the semantic terms; and
assigning a cluster identifier to each of the at least one interest profile segment based on
the at least one semantic representation, wherein the cluster identifier is generated using locality
sensitive hashing (LSH) technique.
2. The method as claimed in claim 1, wherein the method further comprises providing the at
least one interest profile segment and the cluster identifier corresponding to each of the at least
one interest profile segment to a remote node, wherein the remote node is configured to act as an
interest group aggregator for an interest group associated with the cluster identifier, and wherein
the cluster identifier is provided over an anonymous communication channel.
3. The method as claimed in claim 1, wherein the method further comprises:
establishing a plurality of anonymous communication channels with a remote node;
slicing the interest profile into one or more profile slices; and
transmitting the cluster identifier and each of the one or more profile slices to the remote
node over the plurality of anonymous communication channels, wherein each of the one or more
profile slices are transmitted over separate anonymous communication channels.
4. The method as claimed in claim 3, wherein the method further comprises removing from
the interest profile, personally identifiable information of the end user.
5. The method as claimed in claim 1, wherein the method further comprises:
transmitting the cluster identifier over a plurality of anonymous communication channels;
receiving cluster identifiers of other user devices over the plurality of anonymous
communication channels;
identifying, based on the transmitting and the receiving, the other user devices having a
cluster identifier similar to the cluster identifier of the end user; and
enabling exchange of data between the user device of the end user and the other user
devices over the anonymous communication channel.
6. The method as claimed in any of the preceding claims, wherein the assigning further
comprises:
obtaining a dot product between each of the at least one interest profile segment and a set
of hash function; and
concatenating signs of the dot product to obtain a hash code as the cluster identifier
corresponding to the at least one interest profile segment.
7. The method as claimed in any one of the preceding claims, wherein the transforming
further comprises tagging each of the at least one interest profile segment based on the semantic
terms to obtain the at least one semantic representation.
8. A user device (102) comprising:
a processor (202); and
a memory (206) coupled to the processor (202), the memory (206) comprising:
a profile transformation module ( 110) configured to:
obtain semantic terms corresponding to at least one interest profile
segment based on interaction with a semantic metadata database ( 114) coupled to
the user device (102), wherein the semantic terms are obtained from amongst one
or more semantic terms provided in the semantic metadata database ( 114); and
transform each of the at least one interest profile segment into at least one
semantic representation; and
a cluster identifier module ( 112) configured to assign a cluster identifier to each
of the at least one interest profile segment based on the at least one semantic
representation, wherein the cluster identifier is generated using locality sensitive hashing
(LSH) technique.
9. The user device ( 102) as claimed in claim 8, further comprising:
an interest profile generation module (212) configured to generate an interest profile of
an end user associated with the user device (102) based on profile data of the end user; and
a profile segmentation module ( 4) configured to segment the interest profile into the at
least one interest profile segment.
10. The user device (102) as claimed in any one of the preceding claims, further comprising a
data transfer module (216) configured to:
establish an anonymous communication channel (108) with a remote node (104)
configured to act as an interest group aggregator for an interest group associated with the cluster
identifier; and
provide the at least one interest profile segment and the cluster identifier corresponding to
each of the at least one interest profile segments to the remote node (104), wherein the cluster
identifier is provided over the anonymous communication channel (108).
11. The user device (102) as claimed in any one of the claims 8 or 9, further comprising a
data transfer module (2 16) configured to:
establish a plurality of anonymous communication channels (108) with a remote node
(104);
remove personally identifiable information of the end user from the interest profile; and
transmit the cluster identifier and the interest profile of the end user to the remote node
(104) over the plurality of anonymous communication channels (108).
12. The user device (102) as claimed in any one of the claims 8 or 9, further comprising a
data transfer module (216) configured to:
slice the interest profile into one or more profile slices; and
transmit the cluster identifier and each of the one or more profile slices to a remote node
(104) over a plurality of anonymous communication channels (108), wherein each of the one or
more profile slices are transmitted over separate anonymous communication channels (108).
13. The user device (102) as claimed in any one of the claims 8 or 9, further comprising a
data transfer module (216) configured to:
transmit the cluster identifier over a plurality of anonymous communication channels
(108);
receive cluster identifiers of other user devices over the plurality of anonymous
communication channels (108);
identify the other user devices having a cluster identifier similar to the cluster identifier of
the end user based on the transmitting and the receiving; and
enable exchange of data with the other user devices over the anonymous communication
channel (108).
14. The user device (102) as claimed in any one of the claims 8 or 9, further comprising a
data transfer module (2 16) configured to:
obtain at least a partial image of a central aggregator node of interest groups of an end
user of the user device (102);
exchange the cluster identifier with other user devices over a plurality of anonymous
communication channels (108);
identify the other user devices having a cluster identifier similar to the cluster identifier of
the end user based on cluster identifiers of the other user devices received over the plurality of
anonymous communication channels (108); and
provide recommendation services over the anonymous communication channel (108) to
the other user devices identified based on the cluster identifiers.
15. A computer-readable medium having embodied thereon a computer program for
executing a method of privacy protected clustering of interest profiles of end users, the method
comprising:
generating at least one interest profile segment based on an interest profile of an end user;
obtaining semantic terms corresponding to the at least one interest profile segment based
on interaction with a semantic metadata database coupled to a user device of the end user,
wherein the semantic terms are obtained from amongst one or more semantic terms provided in
the semantic metadata database;
transforming each of the at least one interest profile segment into at least one semantic
representation; and
assigning a cluster identifier to each of the at least one interest profile segment based on
the at least one semantic representation, wherein the cluster identifiers are generated using
locality sensitive hashing (LSH) technique.