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Due to the popularity of mobile computing and mobile sensing, users’ traces can now be readily collected to enhance applications’ performance. However, users’ location privacy may be disclosed to the untrusted data aggregator that collects users’ traces. Cloaking users’ traces with synthetic traces is a prevalent technique to protect location privacy. But the existing work that synthesizes traces suffers from the social relationship based de-anonymization attacks. To this end, we propose that synthesizes privacy-preserving traces via enhancing the plausibility of synthetic traces with social networks. The main idea of is to credibly imitate the temporal, spatial, and social behavior of users’ mobility, sample the traces that exhibit similar three-dimension mobility behavior, and synthesize traces using the sampled locations. By doing so, can provide “ differential privacy ” on location privacy preservation …