Drive2friends: Inferring Social Relationships from Individual Vehicle Mobility Data

The number of vehicles has increased year by year, especially individual vehicles. In addition to meeting basic transportation needs, vehicles are expected to serve varied location-based services and applications for humans. However, it can constitute severe risks for privacy. In this paper, we concentrate on one of the most sensitive information, namely social relationships, that can be inferred from the vehicle mobility data. We propose a social relationship inference model, which provides a new perspective for privacy preservation in human mobility data. In particular, we extract discriminative features from both the spatial and temporal dimensions. Then the heterogeneous features are being merged with a fusion model to improve the performance of inference. Extensive experiments on the real-world dataset validate the effectiveness of the extracted features in estimating social connections and demonstrate that our method significantly outperforms the baseline models.