Abstract
Recommender systems are a fundamental component of contemporary social-media platforms and require feedback submitted from users in order to fulfill their goal. On the other hand, the raise of advocacy about user-controlled data repositories supports the selective submission of data by user through intelligent software agents residing at the user end. These agents are endowed with the task of protecting user privacy by applying a “soft filter” on personal data provided to the system. In this work, we pose the question: “how should the software agent control the user feedback submitted to a recommender system in a way that is most privacy preserving, while the user still enjoys most of the benefits of the recommender system?”. We consider a set of such agents, each of which aims to protect the privacy of its serving user by submitting to the recommender system server a version of her real rating profile. The fact that issued recommendations to a user depend on the collective rating profiles by all agents gives rise to a novel game-theoretic setup that unveils the trade-off between privacy preservation of each user and the quality of recommendation they receive. Privacy is quantified through a distance metric between declared and an “initial” random rating profile; the latter is assumed to provide a “neutral” starting point for the disclosure of the real profile. We allow different users to have different perception of their privacy through a user-dependent utility function of this distance. The quality of recommendations for each user depends on submitted ratings of all users, including the ratings of the user to whom the recommendation is provided. We prove the existence of a Nash equilibrium point (NEP), and we derive conditions for that. We show that user strategies converge to the NEP after an iterative best-response strategy update sequence that involves circulation of aggregate quantities in the system and no revelation of real ratings. We also present various modes of user cooperation in rating declaration, by which users mutually benefit in terms of privacy. We evaluate and compare cooperative and selfish strategies in their performance in terms of privacy preservation and recommendation quality through real movie datasets.
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References
Pennacchiotti M, Silvestri F, Vahabi H, Venturini R (2012) Making your interests follow you on Twitter. In: Proceedings of CIKM
Kempe D, Kleinberg JM, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of KDD
Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132
Jeckmans A, Beye M, Erkin Z, Hartel PH, Lagendijk RL, Tang Q (2012) Privacy in recommender systems. In: Ramzan N et al (eds) Social media retrieval. Springer, London, pp 263–281
Halkidi M, Koutsopoulos I (2011) A game theoretic framework for data privacy preservation in recommender systems. In: Proceedings of the PKDD
Nisan N, Roughgarden T, Tardos E, Vazirani VV (2007) Algorithmic game theory. Cambridge University Press, Cambridge
Xu L, Jiang C, Chen Y, Ren Y, Liu KJR (2014) User participation game in collaborative filtering. In: Proceedings of the IEEE conferences signal and information processing (GlobalSIP)
Wang J, Yu L, Zhang W, Yu G, Xu Y, Wang B, Zhang P, Zhang D (2017) IRGAN: a minimax game for unifying generative and discriminative information retrieval models. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (SIGIR’17). ACM, New York, NY, USA. pp 515–524
Parra-Arnau J: Optimized, direct sale of privacy in personal-data marketplaces. arXiv:1701.00740
Melville P, Sindhwani V (2010) Recommender Systems, Encyclopedia of Machine Learning. Springer, New York
Resnick P, Iacovou N, Sushak M, Bergstrom M, Reidl J (1994) Grouplens: an open architecture for collaborative filtering of NETnews. In: Proceedings of computer supported cooperative work conference
Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Advances in Artificial Intelligence, Jan 2009
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Intern Comput 7(1):76–80
Sarwar B, Karypis G, Konstan J, Reidl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the international conference on WWW
Balabanovic M, Shoham Y (1997) Content-based collaborative recommendation. Commun ACM 40(3):66–72
Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: Proceedings of ACM conference on digital libraries
Cotter P, Smyth B (2000) PTV: intelligent personalized TV guides. In: Proceedings of AAAI/IAAI,
Mellville P, Mooney RJ, Nagarajan R (2002) Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the national conference on artificial intelligence
Erkin Z, Beye M, Veugen T, Lagendijk RL (2010) Privacy enhanced recommender system. In: 31st symposium on information theory in the Benelux, WIC 2010. IEEE Benelux Information Theory Chapter, pp 35–42
Nikolaenko V, Ioannidis S, Weinsberg U, Joye M, Taft N, Boneh D (2013) Privacy-preserving matrix factorization. In: Proceedings of the 2013 ACM SIGSAC conference on computer and communications security. New York, NY, USA
Miller B, Konstan JA, Riedl J (2004) Pocketlens: toward a personal recommender system. ACM Trans Inf Syst 22(3):437–476
Polat H, Du W (2003) Privacy-preserving collaborative filtering using randomized perturbation techniques. In: Proceedings of the international conference on data mining (ICDM)
Bilge A, Polat H (2012) An improved privacy-preserving DWT-based collaborative filtering scheme. Expert Syst Appl 39(3):3841–3854
Basu A, Vaidya J, Kikuchi H (2012) Perturbation based privacy preserving slope one predictors for collaborative filtering. In: Dimitrakos T, Moona R, Patel D, McKnight DH (eds) Trust management VI. Springer, Berlin, pp 17–35
Berkovsky S, Eytani Y, Kuflik T, Ricci F (2007) Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Proceedings of the ACM RecSys
Shang S, Huiy Y, Huiz P, Cuff P, Kulkarni S (2013) Privacy preserving recommendation system based on groups. arXiv:1305.0540
Casino F, Domigo-Ferrer J, Patsakis C, Puig D, Solanas A (2015) A \(k\)-anonymous approach to privacy preserving collaborative filtering. J Comput Syst Sci 81(6):1000–1011
Lathia N, Hailes S, Capra L (2007) Private distributed collaborative filtering using estimated concordance measures. In: Proceedings of ACM RecSys
Canny J (2002) Collaborative filtering with privacy. In: Proceedings of the IEEE symposium on security and privacy
Shokri R, Pedarsani P, Theodorakopoulos G, Hubaux JP (2009) Preserving privacy in collaborative filtering through distributed aggregation of offline profiles. In: Proceedings of ACM RecSys
Kandappu T, Friedman A, Boreli R, Sivaraman V (2014) PrivacyCanary: privacy-aware recommenders with adaptive input obfuscation. In Proceedings of the MASCOTS
Bilge A, Polat H (2013) A scalable privacy-preserving recommendation scheme via bisecting \(k\)-means clustering. Inf Process Manag 49(4):912–927
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The authors wish to thank Mrs. Vassiliki Georgoudi and Mr. Panagiotis Spentzouris for their help with part of the numerical experiments.
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Halkidi, M., Koutsopoulos, I. Recommender systems with selfish users. Knowl Inf Syst 62, 3239–3262 (2020). https://doi.org/10.1007/s10115-020-01460-5
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DOI: https://doi.org/10.1007/s10115-020-01460-5