SlopPy: Slope one with privacy

Abstract

In order to contribute to solve the personalization/privacy paradox, we propose a privacy-preserving architecture for one of state-of-the-art recommendation algorithm, Slope One. More precisely, we describe SlopPy (for Slope One with Privacy), a privacy-preserving version of Slope One in which a user never releases directly his personal information (i.e, his ratings). Rather, each user first perturbs locally his information by applying a Randomized Response Technique before sending this perturbed data to a semi-trusted entity responsible for storing it. While there is a trade-off to set between the desired privacy level and the utility of the resulting recommendation, our preliminary experiments clearly demonstrate that SlopPy is able to provide a high level of privacy at the cost of a small decrease of utility. © 2013 Springer-Verlag.