Privacy in Recommender Systems

  • Arjan J. P. JeckmansEmail author
  • Michael Beye
  • Zekeriya Erkin
  • Pieter Hartel
  • Reginald L. Lagendijk
  • Qiang Tang
Part of the Computer Communications and Networks book series (CCN)


In many online applications, the range of content that is offered to users is so wide that a need for automated recommender systems arises. Such systems can provide a personalized selection of relevant items to users. In practice, this can help people find entertaining movies, boost sales through targeted advertisements, or help social network users meet new friends.indent To generate accurate personalized recommendations, recommender systems rely on detailed personal data on the preferences of users. Examples are ratings, consumption histories, and personal profiles. Recommender systems are useful; however, the privacy risks associated to gathering and processing personal data are often underestimated or ignored. Many users are not sufficiently aware if and how much of their data is collected, if such data is sold to third parties, or how securely it is stored and for how long. indent This chapter aims to provide insight into privacy in recommender systems. First, we discuss different types of existing recommender systems. Second, we give an overview of the data that is used in recommender systems. Third, we examine the associated risks to data privacy. Fourth, relevant research areas for privacy-protection techniques and their applicability to recommender systems are discussed. Finally, we conclude with a discussion on applying and combining different privacy-protection techniques in real-world settings, making clear mappings to reflect typical relations between recommender system types, information types, particular privacy risks, and privacy-protection techniques.


Service Provider Recommender System Privacy Concern Homomorphic Encryption Differential Privacy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research for this work was carried out within the Kindred Spirits project, part of the STW Sentinels research program.


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, New York (2011)CrossRefGoogle Scholar
  3. 3.
    Aïmeur, E., Brassard, G., Fernandez, J., Mani Onana, F.: Alambic: a privacy-preserving recommender system for electronic commerce. Int. J. Inf. Secur. 7(5), 307–334 (2008)Google Scholar
  4. 4.
    Basu, A., Kikuchi, H., Vaidya, J.: Privacy-preserving weighted slope one predictor for item-based collaborative filtering. In: Proceedings of the International Workshop on Trust and Privacy in Distributed Information Processing (2011)Google Scholar
  5. 5.
    Basu, A., Vaidya, J., Kikuchi, H.: Efficient privacy-preserving collaborative filtering based on the weighted slope one predictor. J. Internet Serv. Inf. Secur. (JISIS) 1(4), 26–46 (2011)Google Scholar
  6. 6.
    Berkovsky, S., Eytani, Y., Kuflik, T., Ricci, F.: Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Proceedings of the 2007 ACM Conference on Recommender Systems, Minneapolis, pp. 9–16 (2007)Google Scholar
  7. 7.
    Burke, R.: Knowledge-based recommender systems. In: Encyclopedia of Library and Information Systems, vol. 69, pp. 180–200 (2000)Google Scholar
  8. 8.
    Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User Adapt. Interact. 12, 331–370 (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    Canny, J.: Collaborative filtering with privacy. In: IEEE Symposium on Security and Privacy, Oakland, pp. 45–57 (2002)Google Scholar
  10. 10.
    Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proceedings of the 25th Annual International Conference on Research and Development in Information Retrieval, Tampere, pp. 238–245 (2002)Google Scholar
  11. 11.
    Cissée, R., Albayrak, S.: An agent-based approach for privacy-preserving recommender systems. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS ’07, pp. 182:1–182:8. ACM, New York (2007)Google Scholar
  12. 12.
    Cranor, L., Langheinrich, M., Marchiori, M., Presler-Marshall, M., Reagle, J.: The platform for privacy preferences 1.0 (p3p1.0) specification.
  13. 13.
    Dwork, C.: Differential privacy. In: Automata, Languages and Programming, 33rd International Colloquium, ICALP 2006, Venice, 10–14 July 2006, Proceedings, Part II, pp. 1–12 (2006)Google Scholar
  14. 14.
    Erkin, Z., Beye, M., Veugen, T., Lagendijk, R.L.: Privacy enhanced recommender system. In: Thirty-First Symposium on Information Theory in the Benelux, Rotterdam, pp. 35–42 (2010)Google Scholar
  15. 15.
    Erkin, Z., Beye, M., Veugen, T., Lagendijk, R.L.: Efficiently computing private recommendations. In: International Conference on Acoustic, Speech and Signal Processing-ICASSP, Prague, pp. 5864–5867 (2011)Google Scholar
  16. 16.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 61–70 (1992)CrossRefGoogle Scholar
  17. 17.
    Goldreich, O.: Foundations of cryptography: a primer. Found. Trend Theor. Comput. Sci. 1, 1–116 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Gross, R., Acquisti, A.: Information revelation and privacy in online social networks. In: WPES ’05: Proceedings of the 2005 ACM Workshop on Privacy in the Electronic Society, pp. 71–80. ACM, New York (2005)Google Scholar
  19. 19.
    Hoens, T.R., Blanton, M., Chawla, N.V.: A private and reliable recommendation system for social networks. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), Minneapolis, pp. 816–825 (2010)Google Scholar
  20. 20.
    Kang. J.: Information privacy in cyberspace transactions. Stanf. Law Rev. 50(4), 1193–1294 (1998)Google Scholar
  21. 21.
    Kohnstamm, J.: Opinion 2/2010 on online behavioural advertising. Technical report 00909/10/EN WP 171, article 29 data protection working party, 6 2010.
  22. 22.
    Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’09, pp. 195–202. ACM, New York (2009)Google Scholar
  23. 23.
    Lam, S., Frankowski, D., Riedl, J.: Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In: Müller, G. (ed.) Emerging Trends in Information and Communication Security. Lecture Notes in Computer Science, vol. 3995, pp. 14–29. Springer, Berlin/Heidelberg (2006)CrossRefGoogle Scholar
  24. 24.
    Lang, K., Newsweeder: learning to filter netnews. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 331–339. Morgan Kaufmann, San Francisco (1995)Google Scholar
  25. 25.
    Lorenzi, F., Ricci, F.: Case-based recommender systems: a unifying view. In: Intelligent Techniques for Web Personalization. Lecture Notes in Computer Science, vol. 3169, pp. 89–113. Springer, Berlin/Heidelberg (2005)Google Scholar
  26. 26.
    McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the netflix prize contenders. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, pp. 627–636 (2009)Google Scholar
  27. 27.
    Narayanan, A., Shmatikov, V.: How to break anonymity of the netflix prize dataset. In: CoRR: Computing Research Repository, pp. 1–24 (2006)Google Scholar
  28. 28.
    Palen, L., Dourish, P.: Unpacking “privacy” for a networked world. In: CHI ’03: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 129–136. ACM, New York (2003)Google Scholar
  29. 29.
    Polat, H., Du, W.: Svd-based collaborative filtering with privacy. In: Proceedings of the 2005 ACM Symposium on Applied Computing, Santa Fe, pp. 791–795 (2005)Google Scholar
  30. 30.
    Polat, H., Du, W.: Privacy-preserving top-n recommendation on distributed data. J. Am. Soc. Inf. Sci. Technol. 59, 1093–1108 (2008)CrossRefGoogle Scholar
  31. 31.
    Ramakrishnan, N., Keller, B.J., Mirza, B.J., Grama, A.Y., Karypis, G.: Privacy risks in recommender systems. IEEE Internet Comput. 5(6), 54–62 (2001)CrossRefGoogle Scholar
  32. 32.
    Rich, E.: User modeling via stereotypes. Cognit. Sci. 3(4), 329–354 (1979)CrossRefGoogle Scholar
  33. 33.
    Rosenblum, D.: What anyone can know: the privacy risks of social networking sites. IEEE Secur. Priv. 5(3), 40–49 (2007)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Schclar, A., Tsikinovsky, A., Rokach, L., Meisels, A., Antwarg, L.: Ensemble methods for improving the performance of neighborhood-based collaborative filtering. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys ’09, pp. 261–264. ACM, New York (2009)Google Scholar
  35. 35.
    Shokri, R., Pedarsani, P., Theodorakopoulos, G., Hubaux, J.-P.: Preserving privacy in collaborative filtering through distributed aggregation of offline profiles. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys ’09, pp. 157–164. ACM, New York (2009)Google Scholar
  36. 36.
    Sweeney, L.: K-anonymity: a model for protecting privacy. IEEE Secur. Priv. 10(5), 557–570 (2002)MathSciNetzbMATHGoogle Scholar
  37. 37.
    Tsai, J.Y., Egelman, S., Cranor, L., Acquisti, A.: The effect of online privacy information on purchasing behavior: an experimental study. Inf. Syst. Res. 22, 254–268 (2011)CrossRefGoogle Scholar
  38. 38.
    Tufekci, Z.: Can you see me now? audience and disclosure regulation in online social network sites. Bull. Sci. Technol. Soc. 28(1), 20–36 (2008)CrossRefGoogle Scholar
  39. 39.
    Weiss, S.: The need for a paradigm shift in addressing privacy risks in social networking applications. In: The Future of Identity in the Information Society, Brno, vol. 262, pp. hbox161–171. IFIP International Federation for Information Processing (2008)Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Arjan J. P. Jeckmans
    • 1
    Email author
  • Michael Beye
    • 2
  • Zekeriya Erkin
    • 2
  • Pieter Hartel
    • 1
  • Reginald L. Lagendijk
    • 2
  • Qiang Tang
    • 1
  1. 1.Distributed and Embedded Security, Faculty of EEMCSUniversity of TwenteEnschedeThe Netherlands
  2. 2.Information Security and Privacy Lab, Faculty of EEMCSDelft University of TechnologyDelftThe Netherlands

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