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Understanding the linkability of online user identifiers (IDs) is critical to both service providers (for business intelligence) and individual users (for assessing privacy risks). Existing methods are designed to match IDs across two services, but face key challenges of matching multiple services in practice, particularly when users have multiple IDs per service. In this paper, we propose a novel system to link IDs across multiple services by exploring the spatialtemporal locality of user activities. The core idea is that the same user’s online IDs are more likely to repeatedly appear at the same location. Specifically, we first utilize a contact graph to capture the “co-location” of all IDs across multiple services. Based on this graph, we propose a set-wise matching algorithm to discover candidate ID sets, and use Bayesian inference to generate confidence scores for candidate ranking, which is proved to be optimal. We evaluate our system using two real-world ground-truth datasets from an ISP (4 services, 815K IDs) and TwitterFoursquare (2 services, 770 IDs). Extensive results show that our system significantly outperforms the state-of-theart algorithms in accuracy (AUC is higher by 0.1-0.2), and it is highly robust against matching order and number of

You Are How You Move: Linking Multiple User Identities From Massive Mobility Traces