A PRIVACY-PRESERVING FRAMEWORK FOR SECURE ANALYTICS OF HEALTHCARE RECORDS IN MULTI-TENANT CLOUD ENVIRONMENTS

Abstract

Healthcare analytics presents a big difficulty in protecting sensitive data while yet offering insightful information because of the sensitivity of personal health information and the increasing frequency of data breaches. The safe system described in this paper tackles these problems by combining blockchain technology, privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), and a multi-tenant cloud environment. The system uses state-of-the-art cryptographic techniques, specifically zk-SNARKs, to guarantee that healthcare records are safeguarded during analytics computations without revealing raw data. The privacy-preserving analytics engine uses anonymised medical records and creates zk-SNARKs to verify calculations. These proofs create a visible, impenetrable ledger that ensures secure healthcare transactions when integrated into a blockchain network. In situations like telemedicine, when secure data sharing and processing are crucial, this approach is imperative. The framework's practical value in healthcare analytics is demonstrated by its deployment in a telemedicine app, which offers a scalable and secure solution to an urgent problem.