Hybrid Heuristic and Zero-Knowledge Proof Framework for Patient Data Sanitization and Selective Disclosure in Smart Contracts

Abstract

In order to ensure the transparent and immutable maintenance of healthcare data, Blockchain technology has been proposed, but it places privacy against core requirements—one of which is privacy demanded by law. Anonymization techniques present today are useful in providing privacy, however they fall short in this sense in terms of guarantee. While zero-knowledge proofs (ZKPs) are one of the strongest cryptographic attestations, they come with hefty computational costs. This paper proposes an HH-ZKP model within a patient-specific-scope and selective disclosure on a smart contract. On a preliminary note, energetic sorting of electronic health records is actually done where anonymization is achieved using hybrid heuristic methods adhering to the constraints of k-anonymity and l-diversity. This is later followed by succinct ZKPs, ensuring that privacy is being obeyed without exposure of any of the hiding values. Again, the aggregations of these proofs are put onto the block with the smart contract, optimized for gas usage, to increase the scalability to a higher level. It is shown through experimental evaluations on the 10 K synthetic EHR dataset that the proposed scheme shows about a 75% reduced on-chain cost four times reduced proof sizes fully meeting HIPAA Safe Harbor compliance. The HH-ZKP model, by yielding a hybrid of heuristic-preprocessing and formal-principled cryptographic verification, is paving the way for scalable and regulator-aware blockchain health applications.