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Cross-device tracking has drawn growing attention from both commercial companies and the general public because of its privacy implications and applications for user profiling, personalized services, etc. One particular, wide-used type of cross-device tracking is to leverage browsing histories of user devices, e.g., characterized by a list of IP addresses used by the devices and domains visited by the devices. However, existing browsing history based methods have three drawbacks. First, they cannot capture latent correlations among IPs and domains. Second, their performance degrades significantly when labeled device pairs are unavailable. Lastly, they are not robust to uncertainties in linking browsing histories to devices. We propose GraphTrack, a graph-based cross-device tracking framework, to track users across different devices by correlating their browsing histories. Specifically, we propose to model the complex interplays among IPs, domains, and devices as graphs and capture the latent correlations between IPs and between domains. We construct graphs that are robust to uncertainties in linking browsing histories to devices. Moreover, we adapt random walk with restart to compute similarity scores between devices based on the graphs. GraphTrack leverages the similarity scores to perform cross-device tracking. GraphTrack does not require labeled device pairs and can incorporate them if available. We evaluate GraphTrack on two real-world datasets, i.e., a publicly available mobile-desktop tracking dataset (around 100 users) and a multiple-device tracking dataset (154K users) we collected. Our results show that GraphTrack substantially outperforms the state-of-the-art on both datasets., Accepted by AsiaCCS'22

How do public safety net and elite private mental health providers cope with a key dilemma since psychiatric deinstitutionalization—managing madness when people have the right to refuse care? I observed two approaches to voluntary community-based services, one that tolerates “non-compliance” and deviant choices, and another that attempts to therapeutically discipline clientele. The puzzle, given theories of the paternalistic governance of poverty, is that select poor patients are given autonomy while the privileged are micro-managed. Drawing on comparative fieldwork in Los Angeles, I show how contrasting ecological pressures and resource bases shape divergent practices. In the context of urban poverty governance, mental health care and low-barrier housing offer a way to remove problem people from public space. This “tolerant containment” is linked to limited therapeutic capacity and the construction of clients as beyond transformation. In the context of family systems governance, elite private mental health care is a project to reform wayward relatives and equip them with respectable futures. A “concerted constraint” of deviance, akin to Lareau’s theory of privileged childrearing, is reserved for those who can afford rehabilitation and conceivably recover. Using these cases, I contribute to theories of social control and inequality in advanced liberal societies.