  Privacy issues of recommender systems have become a hot topic for the society
as such systems are appearing in every corner of our life. In contrast to the
fact that many secure multi-party computation protocols have been proposed to
prevent information leakage in the process of recommendation computation, very
little has been done to restrict the information leakage from the
recommendation results. In this paper, we apply the differential privacy
concept to neighborhood-based recommendation methods (NBMs) under a
probabilistic framework. We first present a solution, by directly calibrating
Laplace noise into the training process, to differential-privately find the
maximum a posteriori parameters similarity. Then we connect differential
privacy to NBMs by exploiting a recent observation that sampling from the
scaled posterior distribution of a Bayesian model results in provably
differentially private systems. Our experiments show that both solutions allow
promising accuracy with a modest privacy budget, and the second solution yields
better accuracy if the sampling asymptotically converges. We also compare our
solutions to the recent differentially private matrix factorization (MF)
recommender systems, and show that our solutions achieve better accuracy when
the privacy budget is reasonably small. This is an interesting result because
MF systems often offer better accuracy when differential privacy is not
applied.
