Human-in-the-Loop-Aided Privacy-Preserving Scheme for Smart Healthcare
Tianqi Zhou, Jian Shen, Debiao He, Pandi Vijayakumar, Neeraj Kumar
Abstract
Nowadays, artificial intelligence (AI) has become the core technology for numerous application fields ranging from self-driving cars to smart cities. Smart healthcare, as an important part of smart cities, constitutes one of the most essential pillars of social and economic stability. Despite all the possibilities offered by smart healthcare, how to handle the dark aspects of smart healthcare such as security, privacy and trust issues, and so on remains unsolved. In this paper, we focus on designing a human-in-the-loop-aided (HitL-aided) scheme to preserve privacy in smart healthcare. On the one hand, a block design technique is employed to obfuscate various health indicators from the hospitals and the smart wearable devices. On the other hand, human-in-the-loop (HitL) is introduced to enable a privacy access of the health reports from the smart healthcare platform. In addition, the performance analysis and case study indicate that the proposed HitL-aided scheme is effective in preserving privacy for smart healthcare.