A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud Environments

A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud Environments

In the realm of healthcare analytics, preserving the privacy of sensitive data while enabling valuable insights poses a significant challenge, particularly given the increasing prevalence of data breaches and the sensitivity of personal health information. This paper presents a secure framework that...

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Bibliographic Details
Main Authors: S. Bharath Babu, K. R. Jothi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Privacy-preserving analytics
zero-knowledge proofs
blockchain in healthcare
multi-tenant cloud
healthcare security
Online Access:https://ieeexplore.ieee.org/document/10772106/
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Summary:In the realm of healthcare analytics, preserving the privacy of sensitive data while enabling valuable insights poses a significant challenge, particularly given the increasing prevalence of data breaches and the sensitivity of personal health information. This paper presents a secure framework that addresses these concerns by integrating privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), blockchain technology, and a multi-tenant cloud environment. Through advanced cryptographic techniques, specifically zk-SNARKs, the framework ensures that healthcare records remain protected during analytics computations, without exposing raw data. The privacy-preserving analytics engine utilizes anonymized healthcare records and generates zk-SNARKs to validate computations. These proofs, integrated into a blockchain network, create a tamper-proof, transparent ledger that ensures secure healthcare transactions. This approach is critical in scenarios such as telemedicine, where secure data sharing and computation are paramount. By demonstrating its application in a telemedicine app, the framework highlights its practical significance in balancing data utility and privacy in healthcare analytics, providing a scalable and secure solution to a pressing problem.
ISSN:2169-3536

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