An agent-based approach for privacy-preserving recommender systems

Recommender Systems are used in various domains to generate personalized information based on personal user data. The ability to preserve the privacy of all participants is an essential requirement of the underlying Information Filtering architectures, because the deployed Recommender Systems have to be accepted by privacy-aware users as well as information and service providers. Existing approaches neglect to address privacy in this multilateral way. We have developed an approach for privacy-preserving Recommender Systems based on Multiagent System technology which enables applications to generate recommendations via various filtering techniques while preserving the privacy of all participants. We describe the main modules of our solution as well as an application we have implemented based on this approach.

[1]  Olaf Kroll-Peters,et al.  Applying Security Standards to Multi Agent Systems , 2004 .

[2]  Alfred Kobsa,et al.  Impacts of User Privacy Preferences on Personalized Systems , 2004, Designing Personalized User Experiences in eCommerce.

[3]  Pattie Maes,et al.  Political artifacts and personal privacy: the yenta multiagent distributed matchmaking system , 1999 .

[4]  Silvio Micali,et al.  How to play ANY mental game , 1987, STOC.

[5]  Jaideep Vaidya,et al.  Privacy-preserving indexing of documents on the network , 2003, The VLDB Journal.

[6]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[7]  Gustavus J. Simmons,et al.  The Prisoners' Problem and the Subliminal Channel , 1983, CRYPTO.

[8]  Torge Schmidt,et al.  ASITA: advanced security infrastructure for multi-agent applications in the telematic area , 2002 .

[9]  Andreas Rieger,et al.  BerlinTainment: an agent-based context-aware entertainment planning system , 2005, IEEE Communications Magazine.

[10]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[11]  Jacques Ferber,et al.  Environments for Multiagent Systems State-of-the-Art and Research Challenges , 2004, E4MAS.

[12]  John F. Canny,et al.  Collaborative filtering with privacy , 2002, Proceedings 2002 IEEE Symposium on Security and Privacy.

[13]  Eyal Kushilevitz,et al.  Private information retrieval , 1998, JACM.

[14]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[15]  Nicolas Lhuillier,et al.  FOUNDATION FOR INTELLIGENT PHYSICAL AGENTS , 2003 .

[16]  Tal Garfinkel,et al.  Flexible OS Support and Applications for Trusted Computing , 2003, HotOS.

[17]  Bradley N. Miller,et al.  PocketLens: Toward a personal recommender system , 2004, TOIS.

[18]  Yehuda Lindell,et al.  Privacy Preserving Data Mining , 2002, Journal of Cryptology.

[19]  Somesh Jha,et al.  Privacy Preserving Clustering , 2005, ESORICS.

[20]  Richard Cissée DAI-Labor.  An Architecture for Agent-Based Privacy-Preserving Information Filtering , 2003 .

[21]  Gilles Brassard,et al.  Privacy-preserving demographic filtering , 2006, SAC.

[22]  Michael Waidner,et al.  Platform for Enterprise Privacy Practices: Privacy-Enabled Management of Customer Data , 2002, Privacy Enhancing Technologies.

[23]  Jan Keiser,et al.  Agent-based telematic services and telecom applications , 2001, CACM.

[24]  Jared Saia,et al.  The Impact of Social Networks on Multi-Agent Recommender Systems , 2005, ArXiv.

[25]  Ramakrishnan Srikant,et al.  Hippocratic Databases , 2002, VLDB.

[26]  Wenliang Du,et al.  SVD-based collaborative filtering with privacy , 2005, SAC '05.

[27]  Andrew Chi-Chih Yao,et al.  Protocols for secure computations , 1982, FOCS 1982.