Efficient Privacy Protection Protocols for 5G-Enabled Positioning in Industrial IoT

High-accuracy positioning has drawn huge attention with the potential in enhancing location-aware communications, intelligent transportation, and so on. The emergency of the fifth-generation (5G) technologies, such as device-to-device (D2D) communications, vehicle-to-vehicle (V2V) communications, and crowdsourcing networks is expected to help achieve highly accurate positioning. By employing nearby mobile terminals to estimate position cooperatively, these technologies can improve positioning accuracy effectively, especially in indoor and urban areas. Despite the benefit, the potential information disclosure in these positioning systems threatens the engagement of public participants (also known as reference points). The location of the reference points and their distances to a target point is quite sensitive since they can be easily used to locate the reference points once exposed. Though existing solutions based on Paillier homomorphic encryption have been proposed to preserve the privacy of distance information. The sensitivity of reference points' locations is ignored. Additionally, the adoption of Paillier introduces a high computation cost, which is impractical in reality. To address the above problems, this article proposes two efficient protocols, named Pub-pos and Pri-pos. By leveraging matrix concatenation and multiplication, these two protocols can disguise the original sensitive data, including both distance and location information, into a random matrix while keeping a positioning result intact. We analyze the security strength, complexity, and optimal variable selection of the proposed protocols. Numerous experiments verify that our proposed protocols have significant efficiency improvement in both system and individual levels compared with a Paillier-based solution.