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To seek better prediction techniques, data owners of recommender systems such as Netflix sometimes make their customers' reviews available to the public, which raises serious privacy concerns. With only a small amount of knowledge about individuals in a recommender system, an adversary may be able to re-identify users and consequently determine their item ratings. In this paper, the authors present a robust and efficient anonymization algorithm for publishing recommendation datasets, Predictive Anonymization, that gives desired privacy guarantees without significantly affecting prediction accuracy.
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