Linking accounts of the same user across datasets -- even when personally identifying information is removed or unavailable -- is an important open problem studied in many contexts. Beyond many practical applications, (such as cross domain analysis, recommendation, and link prediction), understanding this problem more generally informs us on the privacy implications of data disclosure. Previous work has typically addressed this question using either different portions of the same dataset or observing the same behavior across thematically similar domains. In contrast, the general cross-domain case where users have different profiles independently generated from a common but unknown pattern raises new challenges, including difficulties in validation, and remains under-explored.#R##N##R##N#In this paper, we address the reconciliation problem for location-based datasets and introduce a robust method for this general setting. Location datasets are a particularly fruitful domain to study: such records are frequently produced by users in an increasing number of applications and are highly sensitive, especially when linked to other datasets. Our main contribution is a generic and self-tunable algorithm that leverages any pair of sporadic location-based datasets to determine the most likely matching between the users it contains. While making very general assumptions on the patterns of mobile users, we show that the maximum weight matching we compute is provably correct. Although true cross-domain datasets are a rarity, our experimental evaluation uses two entirely new data collections, including one we crawled, on an unprecedented scale. The method we design outperforms naive rules and prior heuristics. As it combines both sparse and dense properties of location-based data and accounts for probabilistic dynamics of observation, it can be shown to be robust even when data gets sparse.
['Christopher J. Riederer', 'Yunsung Kim', 'Augustin Chaintreau', 'Nitish Korula', 'Silvio Lattanzi']
Linking Users Across Domains with Location Data: Theory and Validation