Efficient Privacy-Friendly and Flexible Wearable Data Processing with User-Centric Access Control

With the advent of cloud computing and the vast amount of data produced by IoT wearable devices, outsourcing computation has become a widespread practice in providing health services to individuals and society. Conventional approaches typically focus on either secure data processing or fine-grain access control. Nevertheless, only a limited number of existing solutions consider secure fine-grain access control over the encrypted computational results. Notably, these solutions overlook data owners’ access control. In addition, they almost exclusively focus on data aggregation operations, neglecting multiplication and division operations on encrypted data, which are fundamental operations with significant importance in various application scenarios. In this paper, we present efficient and privacy-preserving schemes for multiplication and division operations with fine-grain data-sharing and user-centric access control capabilities, called SAMM and SAMD, respectively. We utilise a multi-key Paillier homomorphic cryptosystem to allow privacy-preserving computation of data from both single and multiple data owners. Additionally, we integrate ciphertext-policy attribute-based encryption to enable fine-grain sharing with multiple data requesters based on user-centric access control. Through formal security analysis, we demonstrate that these schemes ensure data confidentiality and authorisation. Moreover, the computational cost and communication overhead of our proposed schemes are thoroughly analysed, and our experimental results indicate that these schemes outperform existing state-of- the-art solutions in terms of efficiency, making them well-suited for use in modern IoT wearable healthcare systems.