DESCRIPTION
The patent disclosure covers System and Method for Privacy-Preserving Techniques for Big Data Analytics in Heatthcare.
In the era of digitized healthcare, the utilization of Big Data analytics has emerged as a transformative force,
offering unprecedented insights for improving patient outcomes, treatment efficacy, and operational efficiency.
However, the vast amounts of sensitive and personal health information involved in these analytics raise
significant privacy concerns. This paper introduces a comprehensive framework of Privacy-Preserving
Techniques for Big Data Analytics in Heatthcare (PPTBDAH) to address these concerns without compromising
the utility of the data.
The proposed framework encompasses state-of-the-art cryptographic techniques, homomorphic encryption,
and differential privacy mechanisms to safeguard patient data during various stages of the analytics pipeline.
By employing a layered approach, PPTBDAH ensures that sensitive information remains encrypted or
anonymized while still allowing for meaningful analysis. The framework is designed to be adaptable to diverse
healthcare scenarios, accommodating a range of data types, including electronic health records, medical
imaging, and genomic data.
PPTBDAH not only focuses on protecting ind1v1dual pnvacy but also lrttroduces novel agg1 egationHs$1Jir'8atele~gi~ees-s ----that
enable collaborative analytics across multiple healthcare entities without sharing raw patient data. The
framework is validated through simulation studies and real-world healthcare datasets, demonstrating its
effectiveness in preserving privacy while maintaining the quality and usefulness of the insights derived from
Big Data analytics.
It contributes a robust foundation for implementing privacy-preserving Big Data analytics in heatthcare,
addressing the critical balance between data utility and individual privacy. As the healthcare industry continues
to embrace data-driven approaches, PPTBDAH serves as a pivotal step towards fostering trust and ensuring
ethical handling of sensitive information in the pursuit of improved healthcare outcomes.
The proposed framework encompasses state-of-the-art cryptographic techniques, homomorphic encryption,
and differential privacy mechanisms to safeguard patient data during various stages of the analytics pipeline.
PPTBDAH not only focuses on protecting individual privacy but also introduces novel aggregation strategies
that enable collaborative analytics across multiple healthcare entities without sharing raw patient data.
It contributes a robust foundation for implementing privacy-preserving Big Data analytics in healthcare,
addressing the critical balance between data utility and individual privacy. As the healthcare industry continues
to embrace data-driven approaches, PPTBDAH serves as a pivotal step towards fostering trust and ensuring
ethical handling of sensitive information in the pursuit of improved health care outcomes.
• It incorporates implementation of advanced cryptographic methods, including homomorphic
encryption, to protect sensitive health data.
• Designing the framework to be adaptable to various types of healthcare data, including electronic
health records (EHRs), medical imaging, genomic data, and other health-related information.
• Ensuring compliance with data protection regulations and healthcare privacy standards.
• The use of advanced cryptographic techniques, such as homomorphic encryption, to secure and
protect sensitive health data.
• Particularly, the present invention related to Integration of differential privacy mechanisms to add
noise or randomness to individual data points, preventing the re-identification of specific individuals
in the dataset.
More particulaMy, the system addressing ethical considerations related to the handling of sensitive
health data.
• The advantage of this technique is to strike a balance between the growing demand for data-driven
healthcare insights and the imperative to protect individual privacy and confidentiality
5. CLAIMS
1/We Claim,
1. The patent disclosure covers System and Method for Privacy-Preserving Techniques for Big Data
Analytics in Healthcare as described above in Fig 1.
2. Secure Multiparty Computation: SMPC is claimed to enable multiple parties to jointly
analyze their datasets without sharing the raw data.
3. Asserted as a powerful approach, differential privacy adds noise to the data to protect
individual privacy while still providing statistically meaningful results.
4. Claimed to be an effective technique, homomorphic encryption allows computations to be
performed on encrypted data without decrypting it.
5. The framework to be adaptable to various types of healthcare data, including electronic
health records (EHRs), medical imaging, genomic data, and other health-related information
6.The Integration of differential privacy mechanisms to add noise or randomness to individual
data points, preventing the re-identification of specific individuals in the dataset.