Semantics-aware location privacy preserving: A differential privacy approach

The protection of location privacy, as a highly sensitive characteristic of information, has been extensively analyzed and discussed for a significant period. Recently, exploiting the semantics of locations offers a new dimension to enhance privacy preservation by enabling more effective control over the information disclosed by users. Different from most prior research efforts, which regard location semantics as a category, in this paper, location semantics is the statistical information about the Points of Interest (PoIs) in the specific location's vicinity, which can be represented as a multi-dimensional vector. Further, Semantic Indistinguishability (SemInd), a more relaxed privacy guarantee for location privacy than Geo-Indistinguishability (Geo-Ind), is derived under the paradigm of differential privacy. Multiple location obfuscation mechanisms, which integrate linear programming and heuristic search, respectively, are proposed to reduce utility loss while ensuring Sem-Ind. Based on the defined utility and privacy metrics, these obfuscation mechanisms are empirically evaluated on the GeoLife dataset. Experimental results indicate that the existing Geo-Ind-based obfuscation mechanisms satisfy Sem-Ind at an excessive loss of utility. Furthermore, the linear programming-based approach is capable of discovering optimal obfuscation functions, whereas the heuristic algorithms are more efficient in obtaining acceptable utility results.