Perturb and optimize users’ location privacy using geo-indistinguishability and location semantics

Abstract

Location-based services (LBS) are capable of providing location-based information retrieval, traffic navigation, entertainment services, emergency rescues, and several similar services primarily on the premise of the geographic location of users or mobile devices. However, in the process of introducing a new user experience, it is also easy to expose users’ specific location which can result in more private information leakage. Hence, the protection of location privacy remains one of the critical issues of the location-based services. Moreover, the areas where humans work and live have different location semantics and sensitivities according to their different social functions. Although the privacy protection of a user’s real location can be achieved by the perturbation algorithm, the attackers may employ the semantics information of the perturbed location to infer a user’s real location semantics in an attempt to spy on a user’s privacy to certain extent. In order to mitigate the above semantics inference attack, and further improve the quality of the location-based services, this paper hereby proposes a user side location perturbation and optimization algorithm based on geo-indistinguishability and location semantics. The perturbation area satisfying geo-indistinguishability is thus generated according to the planar Laplace mechanism and optimized by combining the semantics information and time characteristics of the location. The optimum perturbed location that is able to satisfy the minimum loss of location-based service quality is selected via a linear programming method, and can be employed to replace the real location of the user so as to prevent the leakage of the privacy. Experimental comparison of the actual road network and location semantics dataset manifests that the proposed method reduces approximately 37% perturbation distance in contrast to the other state-of-the-art methods, maintains considerably lower similarity of location semantics, and improves region counting query accuracy by a margin of around 40%.

Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors

“…The comparative analysis of algorithms involved Geo-I [ 9 ], which utilized Euclidean distance; GG-I [ 10 ], which employed undirected graph distance based on Geo-I; and the work of Yan et al [ 11 ] marked as POLS (location perturbation and optimization algorithm), which integrated Geo-I with location semantics while still relying on Euclidean distance. In contrast, our proposed metric incorporates semantic, temporal, and query information alongside road network direction, aiming for enhanced location distinguishability.…”

“…Realistically, there are one-way and two-way streets in real life, and the undirected graph still deviates from the metric of the actual road network. According to different application scenarios, Geo-I has been extensively explored, incorporating various aspects such as location semantics [ 11 ], distribution of personnel directions [ 12 ], and personalized user requirements [ 13 , 14 ], but the distinguishability metric remains consistent with Geo-I.…”

“…Arain et al [ 21 ] proposed an algorithm to protect the information of mobile vehicle users and use geo-indistinguishability to obtain a set of POIs near the source location and destination location. The semantic concept is applied within the framework of the perturbed area optimization algorithm, and a vector represents the location semantics, where the value of row i and column j in the vector represents the number of people who appear in the ith location semantic region in the jth time period in Yan et al [ 11 ]. Kuang et al [ 22 ] point out that location semantics represents the true meaning behind the location and takes a distinctive approach by incorporating the values of location semantics on the road segment based on location semantic road network, considering both user location semantics and query location semantics.…”

“…Temporal information plays a crucial role in privacy protection, as Yan et al [ 11 ] demonstrated, who utilized working time as a temporal characteristic of the location. The location semantic information and temporal relationship are combined to optimize the perturbation area.…”

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

“…Xiong et al 21 combined the location obfuscation of geo-indistinguishability with the path optimization of spatial crowd-sourcing to provide strong privacy protection with minimal cost. Yan et al 22 suggested to combine location semantic information with geo-indistinguishability in order to reduce the probability of semantic inference attack and improve the quality of location service.…”