A differential privacy framework for matrix factorization recommender systems

User Modeling and User-Adapted Interaction

Volumn 26, Issue 5, 2016, Pages 425-458

  Friedman, Arik ^a   Berkovsky, Shlomo ^a   Kaafar, Mohamed Ali ^a  

^a CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; DATA PRIVACY; ECONOMIC AND SOCIAL EFFECTS; FACTORIZATION; RECOMMENDER SYSTEMS;

DIFFERENTIAL PRIVACIES; MATRIX FACTORIZATIONS; PERSONAL INFORMATION; PRIVACY IN STATISTICAL DATABASE; PRIVACY PRESERVING SOLUTIONS; RECOMMENDATION ACCURACY; RECOMMENDATION METHODS; SENSITIVE INFORMATIONS;

MATRIX ALGEBRA;

EID: 84982132275     PISSN: 09241868     EISSN: 15731391     Source Type: Journal    
DOI: 10.1007/s11257-016-9177-7     Document Type: Article

References (44)

1. Berkovsky, S., Eytani, Y., Kuflik, T., Ricci, F.: Hierarchical neighborhood topology for privacy enhanced collaborative filtering. In: Proceedings of Workshop on Privacy-Enhanced Personalization, PEP 2006, Montreal, Canada, pp. 6–13 (2006)
2. Berkovsky, S., Kuflik, T., Ricci, F.: The impact of data obfuscation on the accuracy of collaborative filtering. Expert Systems with Applications 39(5), 5033–5042 (2012)
3. Berlioz, A., Friedman, A., Kâafar, M.A., Boreli, R., Berkovsky, S.: Applying differential privacy to matrix factorization. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, Vienna, Austria, pp. 107–114 (2015). doi:10.1145/2792838.2800173
4. Bhaskar, R., Laxman, S., Smith, A.D., Thakurta, A.: Discovering frequent patterns in sensitive data. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, Washington, DC, USA, pp. 503–512 (2010). doi:10.1145/1835804.1835869
5. Bilge, A., Gunes, I., Polat, H.: Robustness analysis of privacy-preserving model-based recommendation schemes. Expert Systems With Applications 41(8), 3671–3681 (2014)
6. Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E.W., Shmatikov, V.: “You Might Also Like”: Privacy risks of collaborative filtering. In: Proceedings of the 32nd IEEE Symposium on Security and Privacy, S&P 2011, Berkeley, CA, USA, pp. 231–246 (2011). doi:10.1109/SP.2011.40
7. Canny, J.F.: Collaborative filtering with privacy. In: Proceedings of the 23rd IEEE Symposium on Security and Privacy, S&P 2002, Berkeley, CA, USA, pp. 45–57 (2002). doi:10.1109/SECPRI.2002.1004361
8. Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. Journal of Machine Learning Research 12, 1069–1109 (2011)
9. Cheng, Z., Hurley, N.: Trading robustness for privacy in decentralized recommender systems. In: Proceedings of the 21st Conference on Innovative Applications of Artificial Intelligence, IAAI 2009, Pasadena, CA, USA (2009)
10. Dwork, C.: Differential privacy: A survey of results. In: Proceedings of the 5th International Conference on Theory and Applications of Models of Computation, TAMC 2008, Xi’an, China, pp. 1–19 (2008). doi:10.1007/978-3-540-79228-4_1
11. Dwork, C., McSherry, F., Nissim, K., Smith, A.: Differential privacy – a primer for the preplexed. In: Joint UNECE/Eurostat work session on statistical data confidentiality. Tarragona, Spain (2011)
12. Dwork, C., McSherry, F., Nissim, K., Smith, A.D.: Calibrating noise to sensitivity in private data analysis. In: Proceedings of the 3rd Theory of Cryptography Conference, TCC 2006, New York, NY, USA, pp. 265–284 (2006). doi:10.1007/11681878_14
13. Erlingsson, Ú., Pihur, V., Korolova, A.: RAPPOR: Randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, CCS 2014, Scottsdale, AZ, USA, pp. 1054–1067 (2014). doi:10.1145/2660267.2660348
14. Friedman, A., Knijnenburg, B., Vanhecke, K., Martens, L., Berkovsky, S.: Privacy aspects of recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 649–688. Springer, (2015)
15. Friedman, A., Schuster, A.: Data mining with differential privacy. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, Washington, DC, USA, pp. 493–502 (2010). doi:10.1145/1835804.1835868
16. Hardt, M., Talwar, K.: On the geometry of differential privacy. In: Proceedings of the 42nd ACM Symposium on Theory of Computing, STOC 2010, Cambridge, MA, USA, pp. 705–714 (2010). doi:10.1145/1806689.1806786
17. Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems 5(4), 19 (2016)
18. Hay, M., Machanavajjhala, A., Miklau, G., Chen, Y., Zhang, D.: Principled evaluation of differentially private algorithms using DPBench. In: Proceedings of the International Conference on Management of Data, SIGMOD 2016, San Francisco, CA, USA, pp. 139–154 (2016). doi:10.1145/2882903.2882931
19. Jeckmans, A.J., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R.L., Tang, Q.: Privacy in recommender systems. In: Ramzan, N., van Zwol, R., Lee, J.S., Clüver, K., Hua, X.S. (eds.) Social Media Retrieval, pp. 263–281. Springer, (2013)
20. Kifer, D., Machanavajjhala, A.: No free lunch in data privacy. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, 2011, pp. 193–204 (2011). doi:10.1145/1989323.1989345
21. Klösgen, W.: Anonymization techniques for knowledge discovery in databases. In: Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining, KDD 1995, Montreal, Canada, pp. 186–191 (1995)
22. Kobsa, A.: Privacy-enhanced web personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 628–670. Springer, (2007)
23. Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 77–118. Springer, (2015)
24. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
25. Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences 110(15), 5802–5805 (2013)
26. Lam, S.K., Frankowski, D., Riedl, J.: Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In: Proceedings of the International Conference on Emerging Trends in Information and Communication Security, ETRICS 2006, Freiburg, Germany, pp. 14–29 (2006). doi:10.1007/11766155_2
27. Li, T., Unger, T.: Willing to pay for quality personalization? Trade-off between quality and privacy. European Journal of Information Systems 21(6), 621–642 (2012)
28. Machanavajjhala, A., Korolova, A., Sarma, A.D.: Personalized social recommendations - accurate or private? Proceedings of the VLDB Endowment 4(7), 440–450 (2011)
29. 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, KDD 2009, pp. 627–636 (2009). doi:10.1145/1557019.1557090
30. Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: Proceedings of the 29th IEEE Symposium on Security and Privacy, (S&P 2008), Oakland, CA, USA, pp. 111–125 (2008). doi:10.1109/SP.2008.33
31. Netflix spilled your Brokeback Mountain secret. http://www.wired.com/threatlevel/2009/12/netflix-privacy-lawsuit/. Accessed: July 2016
32. Nikolaenko, V., Ioannidis, S., Weinsberg, U., Joye, M., Taft, N., Boneh, D.: Privacy-preserving matrix factorization. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, CCS 2013, Berlin, Germany, pp. 801–812 (2013). doi:10.1145/2508859.2516751
33. Ning, X., Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 37–76. Springer, (2015)
34. Parameswaran, R., Blough, D.M.: Privacy preserving collaborative filtering using data obfuscation. In: Proceedings of the IEEE International Conference on Granular Computing, GrC 2007, San Jose, CA, USA, pp. 380–386 (2007). doi:10.1109/GRC.2007.129
35. Polat, H., Du, W.: Achieving private recommendations using randomized response techniques. In: Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, Singapore, Singapore, pp. 637–646 (2006). doi:10.1007/11731139_73
36. Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook, 2nd edn. Springer, (2015)
37. Said, A., Berkovsky, S., De Luca, E.W., Hermanns, J.: Challenge on context-aware movie recommendation: Camra2011. In: Proceedings of the ACM Conference on Recommender Systems, RecSys 2011, Chicago, IL, USA, pp. 385–386 (2011). doi:10.1145/2043932.2044015
38. Sandhu, R.S., Coyne, E.J., Feinstein, H.L., Youman, C.E.: Role-based access control models. IEEE Computers 29(2), 38–47 (1996)
39. Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the ACM Conference on Electronic Commerce, Minneapolis, MN, USA, pp. 158–167 (2000). doi:10.1145/352871.352887
40. Sun, X., Kashima, H., Matsuzaki, T., Ueda, N.: Averaged stochastic gradient descent with feedback: An accurate, robust, and fast training method. In: Proceedings of the 10th IEEE International Conference on Data Mining, ICDM 2010, Sydney, Australia, pp. 1067–1072 (2010). doi:10.1109/ICDM.2010.26
41. Sweeney, L.: k -anonymity: A model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)
42. Vallet, D., Friedman, A., Berkovsky, S.: Matrix factorization without user data retention. In: Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, Tainan, Taiwan, pp. 569–580 (2014). doi:10.1007/978-3-319-06608-0_47
43. Weinsberg, U., Bhagat, S., Ioannidis, S., Taft, N.: BlurMe: Inferring and obfuscating user gender based on ratings. In: Proceedings of the 6th ACM Conference on Recommender Systems, RecSys 2012, Dublin, Ireland, pp. 195–202 (2012). doi:10.1145/2365952.2365989
44. Zhou, Y., Wilkinson, D.M., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the netflix prize. In: Proceedings of 4th International Conference on Algorithmic Aspects in Information and Management, AAIM 2008, Shanghai, China, pp. 337–348 (2008). doi:10.1007/978-3-540-68880-8_32