Claims:1. A method comprising:
logging for each user, by one or more processors, a user preferred level of granularity for one or more permissions by an application, for managing data in a smart device, the user preferred level of granularity being associated with a selectable pre-defined user profile;
detecting, by the one or more processors, an unwanted flow of data from the application; and
analyzing, by the one or more processors, the user preferred level of granularity with the unwanted flow of data to automatically assign a recommended level of granularity for the one or more permissions.

2. The method of claim 1, wherein logging for each user, a user preferred level of granularity comprises:
receiving user preferences for access control to be associated with the one or more permissions;
associating a level of granularity selected from the group consisting of empty, bogus, anonymized, partial and trusted based on the user preferences; and
generating a user preferred per-permission granularity matrix based on the one or more permissions and the level of granularity associated thereof.

3. The method of claim 2, wherein detecting an unwanted flow of data comprises:
analyzing, by one or more static and dynamic code analyses tools, flow of data from the smart device;
collating output from the one or more static and dynamic code analyses tools to generate a report of flow of data from the smart device;
validating the report of flow of data based on pre-defined requirements pertaining to permissible flow of data in one or more of End-User License Agreement (EULA), application description, application category, application vulnerabilities and destination URL address type to generate a privacy disclosure report; and
generating an actual per-permission granularity matrix based on the privacy disclosure report and corresponding to one or more pre-defined privacy modes.

4. The method of claim 3, wherein analyzing the user preferred level of granularity with the unwanted flow of data comprises generating a matrix for the recommended level of granularity corresponding to the one or more pre-defined privacy modes, based on the user preferred per-permission granularity matrix and the actual per-permission granularity matrix.

5. The method of claim 4 further comprising receiving an over-riding level of granularity over the automatically assigned recommended level of granularity.

6. The method of claim 5 further comprising receiving a selected user profile associated with the over-riding level of granularity.

7. The method of claim 6 further comprising generating a heuristic model based on the user preferred per-permission granularity matrix; the actual per-permission granularity matrix; the matrix for the recommended level of granularity; and the over-riding level of granularity and the selected user profile thereof.

8. A system comprising:
one or more processors;
one or more data storage devices operatively coupled to the one or more processors and configured to store instructions configured for execution by the one or more processors to:
log, for each user a user preferred level of granularity for one or more permissions by an application, for managing data in a smart device, the user preferred level of granularity being associated with a selectable pre-defined user profile;
detect an unwanted flow of data from the application;
analyze the user preferred level of granularity with the unwanted flow of data to automatically assign a recommended level of granularity for the one or more permissions.

9. The system of claim 8, wherein the one or more processors are further configured to log for each user, a user preferred level of granularity by:
receiving user preferences for access control to be associated with the one or more permissions;
associating a level of granularity selected from the group consisting of empty, bogus, anonymized, partial and trusted based on the user preferences; and
generating a user preferred per-permission granularity matrix based on the one or more permissions and the level of granularity associated thereof.

10. The system of claim 9, wherein the one or more processors are further configured to detect an unwanted flow of data by:
analyzing, by one or more static and dynamic code analyses tools, flow of data from the smart device;
collating, output from the one or more static and dynamic code analyses tools to generate a report of flow of data from the smart device;
validating the report of flow of data based on pre-defined requirements pertaining to permissible flow of data in one or more of End User License Agreement (EULA), application description, application category, application vulnerabilities and destination URL address type to generate a privacy disclosure report; and
generating an actual per-permission granularity matrix based on the privacy disclosure report and corresponding to one or more pre-defined privacy modes.

11. The system of claim 10, wherein the one or more processors are further configured to generate a matrix for the recommended level of granularity corresponding to the one or more pre-defined privacy modes, based on the user preferred per-permission granularity matrix and the actual per-permission granularity matrix.

12. The system of claim 11, wherein the one or more processors are further configured to receive an over-riding level of granularity over the automatically assigned recommended level of granularity.

13. The system of claim 12, wherein the one or more processors are further configured to receive a selected user profile associated with the over-riding level of granularity.

14. The system of claim 13, wherein the one or more processors are further configured to generate a heuristic model based on the user preferred per-permission granularity matrix; the actual per-permission granularity matrix; the matrix for the recommended level of granularity; and the over-riding level of granularity and the selected user profile thereof.
, Description:As Attached