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.
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Gambs, S., Lolive, J. (2013). SlopPy: Slope One with Privacy. In: Di Pietro, R., Herranz, J., Damiani, E., State, R. (eds) Data Privacy Management and Autonomous Spontaneous Security. DPM SETOP 2012 2012. Lecture Notes in Computer Science, vol 7731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35890-6_8
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DOI: https://doi.org/10.1007/978-3-642-35890-6_8
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