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

Abstract

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.

Keywords

• Privacy-preserving analytics
• zero-knowledge proofs
• blockchain in healthcare
• multi-tenant cloud
• healthcare security