SEPSI: A Secure and Efficient Privacy-Preserving Set Intersection with Identity Authentication in IoT

Abstract

The rapid development of the Internet of Things (IoT), big data and artificial intelligence (AI) technology has brought extensive IoT services to entities. However, most IoT services carry the risk of leaking privacy. Privacy-preserving set intersection in IoT is used for a wide range of basic services, and its privacy protection issues have received widespread attention. The traditional candidate protocols to solve the privacy-preserving set intersection are classical encryption protocols based on computational difficulty. With the emergence of quantum computing, some advanced quantum algorithms may undermine the security and reliability of traditional protocols. Therefore, it is important to design more secure privacy-preserving set intersection protocols. In addition, identity information is also very important compared to data security. To this end, we propose a quantum privacy-preserving set intersection protocol for IoT scenarios, which has higher security and linear communication efficiency. This protocol can protect identity anonymity while protecting private data.