Differentially private neighborhood-based recommender systems

Auteurs

J. Wang and Q. Tang

Référence

in ICT Systems Security and Privacy Protection: 32nd IFIP TC 11 International Conference (SEC 2017), Rome, Italy, May 29-31, 2017, Proceedings, S. De Capitani di Vimercati and F. Martinelli (Eds.), Cham: Springer, pp. 459-473, 2017

Description

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.

Lien

DOI: 10.1007/978-3-319-58469-0_31