Social Relationship Inference over Private Vehicle Mobility Data

The ubiquity of private vehicles with positioning services leaves a great deal of mobility data in the physical world, which supports abundant mobile applications in the Internet of Vehicles. Despite numerous desirable features that the data provide, the social relationship privacy inherent in private vehicle mobility data has gone with little notice. The relevant work that concentrates on social relation privacy either only considers the temporal and spatial features, attempts to obtain the explicit venue cooccurrence frequency statistics of mobility data, or characterizes the semantics of locations by directly matching the POI information to the location. In this paper, we propose a \underline{SE}mantic-aware and \underline{M}emory-\underline{E}fficient scheme (SEME) for inferring social relationships from private vehicle mobility data. We determine the probability distribution of the visit purpose for each stopover location in vehicle trajectories by a probabilistic generative model with a latent variable of semantic feature vectors that embeds the semantic information, time context, and correlations. Labeling the trajectories with visit purposes, we derive a mobility feature vector for each driver with a feature learning model. Based on the mobility feature vectors, the similarity score that indicates the social connection strength can be pairwise-computed with an effective measurement, i.e., the cosine similarity. Additionally, for both scalability and computational efficiency, we convert the large-scale similarity calculation to an extended version of a maximum inner-product search problem and derive the closed-form solution of binary codes which is used to approximately solve the problem. To evaluate the performance of SEME, we conduct experiments with respect to effectiveness, efficiency, and robustness based on real-world private vehicle trajectories. The evaluation results demonstrate that our scheme improves the inference accuracy and reduces the time cost of similarity calculation.