Privacy Concerns when Modeling Users in Collaborative Filtering Recommender Systems

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Author(s): Sylvain Castagnos (LORIA—Université Nancy 2, Campus Scientifique, France)
Copyright: 2009
Pages: 14
Source title: Social and Human Elements of Information Security: Emerging Trends and Countermeasures
Source Author(s)/Editor(s): Manish Gupta (State University of New York, USA)and Raj Sharman (State University of New York, USA)
DOI: 10.4018/978-1-60566-036-3.ch014

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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Abstract

This chapter investigates ways to deal with privacy rules when modeling preferences of users in recommender systems based on collaborative filtering. It argues that it is possible to find a good compromise between quality of predictions and protection of personal data. Thus, it proposes a methodology that fulfills with strictest privacy laws for both centralized and distributed architectures. The authors hope that their attempts to provide a unified vision of privacy rules through the related works and a generic privacy-enhancing procedure will help researchers and practitioners to better take into account the ethical and juridical constraints as regards privacy protection when designing information systems.

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Privacy Concerns when Modeling Users in Collaborative Filtering Recommender Systems

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