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Achieving Optimal Privacy in Trust-Aware Social Recommender Systems

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Book cover Social Informatics (SocInfo 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6430))

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Abstract

Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers proposed for utilization of interpersonal trust between users, to alleviate many of these crucial shortcomings. Till now, attention has been mainly paid to strong points about trust-aware recommenders such as alleviating profile sparsity or calculation cost efficiency, while least attention has been paid on investigating the notion of privacy surrounding the disclosure of individual ratings and most importantly protection of trust computation across social networks forming the backbone of these systems. To contribute to addressing problem of privacy in trust-aware recommenders, within this paper, first we introduce a framework for enabling privacy-preserving trust-aware recommendation generation. While trust mechanism aims at elevating recommender’s accuracy, to preserve privacy, accuracy of the system needs to be decreased. Since within this context, privacy and accuracy are conflicting goals we show that a Pareto set can be found as an optimal setting for both privacy-preserving and trust-enabling mechanisms. We show that this Pareto set, when used as the configuration for measuring the accuracy of base collaborative filtering engine, yields an optimized tradeoff between conflicting goals of privacy and accuracy. We prove this concept along with applicability of our framework by experimenting with accuracy and privacy factors, and we show through experiment how such optimal set can be inferred.

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References

  1. Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: Group-Lens: An open architecture for collaborative filtering of netnews. In: Proceedings of the ACM 1994 Conference on Computer-Supported Cooperative Work, pp. 175–186. ACM, Chapel Hill (1994)

    Google Scholar 

  2. Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  3. Cranor, L.F.: ’I didn’t buy it for myself’ privacy and e-commerce personalization. In: Proceedings of the ACM Workshop on Privacy in the Electronic Society, pp. 111–117 (2003)

    Google Scholar 

  4. Canny, J.: Collaborative filtering with privacy. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. 45–57 (2002)

    Google Scholar 

  5. Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, pp. 238–245 (2002)

    Google Scholar 

  6. Du, W., Polat, H.: Privacy-preserving collaborative filtering. International Journal of Electronic Commerce 9(4), 9–36 (2005)

    Google Scholar 

  7. Kaleli, C., Polat, H.: Providing private recommendations using naive Bayesian classifier. Advances in Intelligent Web Mastering 43, 515–522 (2007)

    Google Scholar 

  8. Kaleli, C., Polat, H.: P2P collaborative filtering with privacy. Turkish Journal of Electric Electrical Engineering and Computer Sciences 8(1), 101–116 (2010)

    Google Scholar 

  9. Kaleli, C., Polat, H.: Providing private recommendations on personal social networks. Advances in Soft Computing 67, 117–125 (2010)

    Article  Google Scholar 

  10. Parameswaran, R.: A robust data obfuscation approach for privacy-preserving collaborative filtering. PhD thesis, Georgia Institute of Technology (2006)

    Google Scholar 

  11. Eytani, Y., Kuflik, T., Berkovsky, S., Busetta, P., Ricci, F.: Collaborative filtering over distributed environment. In: Workshop on Decentralized, Agent-based and social Approaches to User Modeling, in conjunction with the 10th International Conference on User Modeling, Edinburg, UK (2005)

    Google Scholar 

  12. Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW 2005: Proceedings of the 14th international conference on World Wide Web, pp. 22–32. ACM, New York (2005)

    Google Scholar 

  13. Golbeck, J.A.: Filmtrust: movie recommendations from semantic web-based social networks. In: Proc. of 3rd IEEE Consumer Communications and Networking Conference, CCNC 2006, Department of Computer Science, University of Maryland, pp. 1314–1315 (2006)

    Google Scholar 

  14. Golbeck, J.: Trust and nuanced profile similarity in online social networks, MINDSWAP Technical Report TR-MS1284, University of Maryland, College Park, Tech. Rep. (2007)

    Google Scholar 

  15. Massa, P., Avesani, P.: Trust-Aware Collaborative filtering for Recommender Systems. In: On the Move to Meaningful Internet Systems: CoopIS, DOA, and ODBASE, pp. 492–508 (2004)

    Google Scholar 

  16. Massa, P., Avesani, P.: Trust Metrics in Recommender Systems. In: Computing with Social Trust, pp. 259–285 (2009)

    Google Scholar 

  17. O’Donovan, J., Smyth, B.: Trust in recommender systems. In: IUI 2005: Proceedings of the 10th international conference on intelligent user interfaces, pp. 167–174. ACM, New York (2005)

    Google Scholar 

  18. Lathia, N., Hailes, S., Capra, L.: Trust-based collaborative filtering. In: IFIPTM 2008: Joint iTrust and PST Conferences on Privacy, Trust management and Security, Department of Computer Science, University College London, London, UK, p. 14 (2008)

    Google Scholar 

  19. Zarghami, A., Fazeli, S., Dokoohaki, N., Matskin, M.: Social Trust-Aware Recommendation System: A T-Index Approach. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 85–90. IEEE Computer Society, Los Alamitos (2009), doi:10.1109/WI-IAT.2009.237

    Google Scholar 

  20. Squicciarini, A., Bertino, E., Ferrari, E., Paci, F., Thuraisingham, B.: PP-trust-X. ACM Transactions on Information and System Security 10(3), 12-es (2007), doi:10.1145/1266977.1266981

    Google Scholar 

  21. Singh, A., Liu, L.: TrustMe: anonymous management of trust relationships in decentralized P2P systems. In: Proc. Third Int’l IEEE Conf. on Peer-to-Peer Computing (2003)

    Google Scholar 

  22. Lam, S.K., Frankowski, D., Riedl, J.(n.d): Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems, pp. 1–15. The New York Times

    Google Scholar 

  23. Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7(4) (2007), doi:10.1145/1278366.1278372

    Google Scholar 

  24. Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit, pp. 393–402. ACM, New York (2004), doi:10.1145/988672.988726

    Google Scholar 

  25. Zhang, F.: Average shilling attack against trust-based recommender systems. In: International Conference on Information Management, Innovation Management and Industrial Engineering, vol. 4, pp. 588–591 (2009)

    Google Scholar 

  26. Massa, P., Avesani, P.: Controversial users demand local trust metrics: An experimental study on epinions.com community. In: Proceedings of the National Conference on artificial Intelligence, vol. 20, p. 121. AAAI Press/MIT Press, Menlo Park/Cambridge (1999)

    Google Scholar 

  27. Hirsch, J.E.: An index to quantify an individual’s scientific research output. PNAS 102(46), 16569–16572 (2005)

    Google Scholar 

  28. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the l4h Conference on Uncertainty in Artificial Intelligence (UAI 1998), San Francisco, July 24-26, pp. 43–52 (1998)

    Google Scholar 

  29. Dokoohaki, N., Matskin, M.: Effective design of trust ontologies for improvement in the structure of socio-semantic trust networks. International Journal On Advances in Intelligent Systems 1(1942-2679), 23–42 (2008)

    Google Scholar 

  30. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)

    Google Scholar 

  31. Fazeli, S., Zarghami, A., Dokoohaki, N., Matskin, M.: Mechanizing Social Trust-Aware Recommenders with T-index Augmented Trustworthiness. In: Proceedings of the 7th International Conference on Trust, Privacy & Security in Digital Business (Trustbus 2010), in conjunction with the 21st International Conference on Database and Expert Systems Applications (DEXA 2010), Bilbao, Spain (2010)

    Google Scholar 

  32. Fazeli, S., Zarghami, A., Dokoohaki, N., Matskin, M.: Elevating Prediction Accuracy in Trust-aware Collaborative filtering Recommenders through T-index Metric and TopTrustee lists. The Journal of Emerging Technologies in Web Intelligence, JETWI (2010)

    Google Scholar 

  33. Riedl, J., Konstan, J.: Movielens Dataset (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics, Computer and Software Systems, Royal Institute of Technology (KTH), 16440, Kista, Stockholm, Sweden

    Nima Dokoohaki & Mihhail Matskin

  2. Department of Computer Engineering, Anadolu University, Eskisehir, 26470, Turkey

    Cihan Kaleli & Huseyin Polat

  3. Department of Information and Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Mihhail Matskin

Authors
  1. Nima Dokoohaki
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  2. Cihan Kaleli
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  3. Huseyin Polat
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  4. Mihhail Matskin
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Editor information

Editors and Affiliations

  1. Polish-Japanese Institute of Information Technology, 86 Koszykowa, 02-008, Warsaw, Poland

    Leonard Bolc  & Adam Wierzbicki  & 

  2. International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1., 2361, Laxenburg, Austria

    Marek Makowski

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Dokoohaki, N., Kaleli, C., Polat, H., Matskin, M. (2010). Achieving Optimal Privacy in Trust-Aware Social Recommender Systems. In: Bolc, L., Makowski, M., Wierzbicki, A. (eds) Social Informatics. SocInfo 2010. Lecture Notes in Computer Science, vol 6430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16567-2_5

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  • DOI: https://doi.org/10.1007/978-3-642-16567-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16566-5

  • Online ISBN: 978-3-642-16567-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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