Location Privacy-Preserving Method Based on Historical Proximity Location

Abstract

With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users’ location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as k-anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.

An efficient algorithm for data transmission certainty in IIoT sensing network: A priority-based approach

“…The security data protection method provides a security threat assessment method in actual application scenarios, defines the target data set, and proposes a low-overhead protection framework. In addition, from the perspective of location privacy data protection of the IoT, reference [ 61 ] proposed location service application scenarios, system framework, etc., and then the attacker model and privacy protection metrics in LBS are discussed, and finally potential research directions for future LBS privacy protection technology are given. Reference [ 62 ] relies on mobile devices sensor on the mobile phone explores the derivation method of the user’s location and path, and aggregates the data in the sensor to design a semantic aggregation algorithm of the user’s location and user characteristics rules, which poses a challenge to the location privacy protection on the mobile device from the perspective of attack.…”

PriADA: Management and Adaptation of Information Based on Data Privacy in Public Environments

“…Considering that mobile devices have such data, there is a need to control and manage how it is done [2]. Several studies have been done about the privacy and security of Internet of Things technologies, because it is a crucial subject where the security is essential as it mainly uses distributed and fog computing [9][10][11][12] and the comparison of several algorithms [13,14]. The use of mobile applications is also expanding, and the privacy of the data inserted on them should be controlled [15,16].…”