Privacy-Preserving Implicit Authentication Protocol Using Cosine Similarity for Internet of Things

Abstract

Internet of Things provides complicated value-added services to mobile intelligent terminal users. Different sensors collect various data from the users and transmit the data to the mobile intelligent terminal for storage. Consequently, a great amount of personal and sensitive information related to these rich and colorful applications is stored in the mobile intelligent terminal. Mobile intelligent terminals have become the prominent target of network attackers. Security breach and privacy leakage severely thread the application development of the Internet of Things. We present a privacy-preserving implicit authentication framework using users' behavior features sensed by the mobile intelligent terminal based on the artificial intelligence methodology. More precisely, we first summarize the security and privacy requirements for the security authentication of the mobile intelligent terminal. Then, we present a privacy-preserving implicit authentication framework using the cosine similarity and partial homomorphic public-key encryption scheme. Finally, a performance evaluation of the proposed protocol is conducted. The result shows that the communication and computation efficiency of our protocol is more efficient than other related protocols.