An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services

Abstract

With the rapid development of GPS-equipped smart mobile devices and mobile computing, location-based services (LBS) are increasing in popularity in the Internet of Things (IoT). Although LBS provide enormous benefits to users, they inevitably introduce some significant privacy concerns. To protect user privacy, a variety of location privacy-preserving schemes have been recently proposed. Among these schemes, the dummy-based location privacy-preserving (DLP) scheme is a widely used approach to achieve location privacy for mobile users. However, the computation cost of the existing dummy-based location privacy-preserving schemes is too high to meet the practical requirements of resource-constrained IoT devices. Moreover, the DLP scheme is inadequate to resist against an adversary with side information. Thus, how to effectively select a dummy location is still a challenge. In this paper, we propose a novel lightweight dummy-based location privacy-preserving scheme, named the enhanced dummy-based location privacy-preserving(Enhanced-DLP) to address this challenge by considering both computational costs and side information. Specifically, the Enhanced-DLP adopts an improved greedy scheme to efficiently select dummy locations to form a k-anonymous set. A thorough security analysis demonstrated that our proposed Enhanced-DLP can protect user privacy against attacks. We performed a series of experiments to verify the effectiveness of our Enhanced-DLP. Compared with the existing scheme, the Enhanced-DLP can obtain lower computational costs for the selection of a dummy location and it can resist side information attacks. The experimental results illustrate that the Enhanced-DLP scheme can effectively be applied to protect the user’s location privacy in IoT applications and services.

Obfuscation-Based Mechanisms in Location-Based Privacy Protection

“…The dummies they generated considered the geography of the user, and interestingly, they were able to hide the users well, but not where real-life issues are concerned. This is confirmed by Du et al [9], "side-information" is available to attackers and can reveal the user. The spoofing method has advantages over the others in that the recommender results are generally accurate [1].…”

“…The spoofing method has advantages over the others in that the recommender results are generally accurate [1]. In the Du et al [9] study, entropy was not lost using their Enhanced Dummy-Based Location Privacy. However, this method had issues with being an intense process, needing to send many bogus signals, and on the receiving end, the location-based servers have to deal with the spoofing impact [1].…”

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

“…Researchers filtered them for multi-dimensional similarity to get virtual locations that are more similar to real locations. Literature [ 36 ] presents Enhanced-DLP, a novel, lightweight false location privacy protection scheme, aimed at tackling computational cost and additional information attacks challenges found in traditional schemes. Enhanced-DLP efficiently selects false locations to create k -anonymous sets using an improved greedy algorithm.…”