A Verifiable Privacy-Preserving Outsourced Prediction Scheme Based on Blockchain in Smart Healthcare

Ta Li, Youliang Tian, Jinbo Xiong, Linjie Wang

Abstract: The swift progression of the Internet of Things and the extensive integration of machine learning have spurred the growth of intelligent healthcare. Many intelligent healthcare devices, limited by their own computing and storage resources, require outsourcing data analysis tasks to cloud platforms for efficient and accurate results. Unfortunately, malicious cloud services lead to privacy breaches in outsourced data and untrustworthiness in learning models. To address these challenges, this paper proposes a verifiable privacy-preserving outsourced prediction scheme based on blockchain in smart healthcare (VPOL). Specifically, by incorporating blockchain technology into VPOL, we build a robust and scalable framework to prevent falsification of outsourced data and learning models in a decentralized and transparent manner. Then, we design a training committee approach to ensure the reliability of outsourced prediction and employ homomorphic encryption and commitment scheme to protect the privacy and integrity of the data. Finally, theoretical analysis proves the effectiveness and security of VPOL. Sufficient experiments demonstrate that VPOL achieves the approximate accuracy of the plaintext.