RASS: Enabling privacy-preserving and authentication in online AI-driven healthcare applications

Powered by the rapid progress of analytics techniques and the increasing availability of healthcare data, artificial intelligence (AI) is bringing a paradigm shift to healthcare applications. AI techniques offer considerable advantages for the evaluation and assimilation of large amounts of complex healthcare data. However, to effectively use AI tools in healthcare, key issues need to be considered and several limitations must be addressed, such as privacy-preserving and authentication of the healthcare data for analysis in training and inference procedures. Although various techniques ranging from cryptographic tools to obfuscation mechanisms have been proposed to provide privacy guarantees for data in AI-based services, none of them is applicable to online AI-driven healthcare applications. For they require a heavy computational cost on protecting privacy without offering authentication services for third parties. In this paper, we present RASS, an efficient privacy-preserving and authentication scheme for securing analyzed data in an AI-driven healthcare system. The security proofs of our construction indicate that its unforgeability and multi-show unlinkability can defend against the tempering and collusion attacks respectively. Finally, we conduct sufficient efficiency analysis, and the results show that RASS achieves the above security demands without introducing complex computation and communication costs.