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Privacy Concerns when Modeling Users in Collaborative Filtering Recommender Systems

Sylvain Castagnos 1, 2 Anne Boyer 1, 2
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 KIWI - Knowledge Information and Web Intelligence
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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 an 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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https://hal.inria.fr/inria-00171806
Contributor : Sylvain Castagnos <>
Submitted on : Thursday, September 13, 2007 - 11:44:34 AM
Last modification on : Friday, February 26, 2021 - 3:28:08 PM

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  • HAL Id : inria-00171806, version 1

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Sylvain Castagnos, Anne Boyer. Privacy Concerns when Modeling Users in Collaborative Filtering Recommender Systems. Manish Gupta and Raj Sharman. Social and Human Elements of Information Security: Emerging Trends and Countermeasures, IdeaGroup, Inc., 2008. ⟨inria-00171806⟩

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