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The paper considers the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users. Specifically, the paper considers the Netflix Prize data set, and its leading algorithms, adapted to the framework of differential privacy. Unlike prior privacy work concerned with cryptographically securing the computation of recommendations, differential privacy constrains a computation in a way that precludes any inference about the underlying records from its output. Such algorithms necessarily introduce uncertainty - i.e., noise - to computations, trading accuracy for privacy. The paper finds that several of the leading approaches in the Netflix Prize competition can be adapted to provide differential privacy, without significantly degrading their accuracy.
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