Blockchain-Enhanced Data Privacy Preservation and Secure Sharing Scheme for Healthcare IoT
Shaopeng Guan, Youliang Cao, Yuan Zhang
Abstract
Data privacy preservation and secure sharing are key technical challenges faced by smart wearable healthcare Internet of Things (IoT) systems. Blockchain technology enables privacy preservation for medical data through encryption. However, conventional data encryption hampers data analysis and sharing, and decrypted data still carries the risk of leakage. Homomorphic encryption is a technique that allows computation directly on encrypted data without decryption, thus reducing the risk of data leakage during sharing. In this article, we propose a blockchain-based privacy preservation and sharing scheme for healthcare IoT data. First, we use an improved homomorphic encryption technique to encrypt and process electronic health records (EHRs), optimizing the modular exponentiation process with a fast exponentiation algorithm, enabling users to efficiently perform data computation and analysis while keeping the data encrypted. Second, we employ symmetric searchable encryption (SSE) to encrypt homomorphic keys and user identity information, and use a Bloom filter as the mapping structure between data keywords and unique identifiers. This approach enhances search efficiency while preserving data privacy, allowing for secure search and analysis on ciphertext. Finally, smart contracts are designed to implement access control during the data-sharing process, increasing the security and transparency of data sharing. Experimental results show that the proposed homomorphic encryption scheme reduces the encryption and decryption time by an average of 34% under different key sizes, while the optimized SSE technique keeps ciphertext retrieval time at a constant level. The proposed scheme provides an effective solution for secure and efficient data analysis and retrieval, ensuring privacy preservation for the secure use and sharing of medical data.