Advertisement

Towards privacy preserving social recommendation under personalized privacy settings

  • Xuying Meng
  • Suhang Wang
  • Kai Shu
  • Jundong Li
  • Bo Chen
  • Huan Liu
  • Yujun Zhang
Article
  • 70 Downloads
Part of the following topical collections:
  1. Special Issue on Social Computing and Big Data Applications

Abstract

Privacy leakage is an important issue for social relationships-based recommender systems (i.e., social recommendation). Existing privacy preserving social recommendation approaches usually allow the recommender to fully control users’ information. This may be problematic since the recommender itself may be untrusted, leading to serious privacy leakage. Besides, building social relationships requires sharing interests as well as other private information, which may lead to more privacy leakage. Although sometimes users are allowed to hide their sensitive private data using personalized privacy settings, the data being shared can still be abused by the adversaries to infer sensitive private information. Supporting social recommendation with least privacy leakage to untrusted recommender and other users (i.e., friends) is an important yet challenging problem. In this paper, we aim to achieve privacy-preserving social recommendation under personalized privacy settings. We propose PrivSR, a novel privacy-preserving social recommendation framework, in which user can model user feedbacks and social relationships privately. Meanwhile, by allocating different noise magnitudes to personalized sensitive and non-sensitive feedbacks, we can protect users’ privacy against untrusted recommender and friends. Theoretical analysis and experimental evaluation on real-world datasets demonstrate that our framework can protect users’ privacy while being able to retain effectiveness of the underlying recommender system.

Keywords

Differential privacy Social recommendation Ranking Personalized privacy settings 

Notes

Acknowledgments

This work is supported by, or in part by, National Science Foundation of China (61672500, 61572474), and Program of International S&T Cooperation (2016YFE0121500). Suhang Wang and Huan Liu are supported by the National Science Foundation (NSF) under the grant #1614576 and Office of Naval Research (ONR) under the grant N00014-16-1-2257.

References

  1. 1.
    Bhamidipati, S., Fawaz, N., Kveton, B., Zhang, A.: Priview: Personalized media consumption meets privacy against inference attacks. IEEE Softw. 32(4), 53–59 (2015)CrossRefGoogle Scholar
  2. 2.
    Cao, Y., Yoshikawa, M., Xiao, Y., Xiong, L.: Quantifying differential privacy under temporal correlations. In: 33rd IEEE International Conference on Data Engineering, ICDE 2017. San Diego, CA, USA, April 19-22, 2017, pp. 821–832.  https://doi.org/10.1109/ICDE.2017.132 (2017)
  3. 3.
    Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. J. Mach. Learn. Res. 12, 1069–1109 (2011)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010. Barcelona, Spain, September 26-30, 2010, pp. 39–46.  https://doi.org/10.1145/1864708.1864721 (2010)
  5. 5.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Theory of Cryptography Conference, pp. 265–284. Springer (2006)Google Scholar
  6. 6.
    Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends. Theor. Comput. Sci. 9(3–4), 211–407 (2014)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Eisenberg, B., Sullivan, R.: Why is the sum of independent normal random variables normal? Math. Mag. 81(5), 362–366 (2008)CrossRefzbMATHGoogle Scholar
  8. 8.
    Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., Ristenpart, T.: Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing. In: Proceedings of the 23rd USENIX Security Symposium, San Diego, CA, USA, August 20-22, 2014., pp. 17–32 (2014)Google Scholar
  9. 9.
    Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: ACM Sigsac Conference on Computer and Communications Security, pp. 1322–1333 (2015)Google Scholar
  10. 10.
    Gross, R., Acquisti, A.: Information revelation and privacy in online social networks. In: Proceedings of the Acm Workshop on Privacy in the Electronic Society, pp. 71–80 (2005)Google Scholar
  11. 11.
    Hoens, T.R., Blanton, M., Chawla, N.V.: A private and reliable recommendation system for social networks. In: IEEE Second International Conference on Social Computing, pp. 816–825 (2010)Google Scholar
  12. 12.
    Hua, J., Xia, C., Zhong, S.: Differentially private matrix factorization. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015. Buenos Aires, Argentina, July 25-31, 2015, pp. 1763–1770. http://ijcai.org/Abstract/15/251 (2015)
  13. 13.
    Jorgensen, Z., Yu, T.: A privacy-preserving framework for personalized, social recommendations. In: International Conference on Extending Database Technology, EDBT (2014)Google Scholar
  14. 14.
    Kasiviswanathan, S.P., Rudelson, M., Ullman, J.: The price of privately releasing contingency tables and the spectra of random matrices with correlated rows. In: ACM Symposium on Theory of Computing, pp. 775–784 (2010)Google Scholar
  15. 15.
    Komarova, T., Nekipelov, D., Yakovlev, E.: Estimation of treatment effects from combined data: Identification versus data security. In: Iccas-Sice, pp. 3066–3071 (2013)Google Scholar
  16. 16.
    Koren, Y.: Factorization meets the neighborhood: A multifaceted collaborative filtering model. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434 (2008)Google Scholar
  17. 17.
    Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53 (4), 89–97 (2010)CrossRefGoogle Scholar
  18. 18.
    Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009).  https://doi.org/10.1109/MC.2009.263 CrossRefGoogle Scholar
  19. 19.
    Kotz, S., Kozubowski, T., Podgorski, K.: The Laplace Distribution and Generalizations: A Revisit with Applications to Communications, Economics, Engineering, and Finance. Springer Science & Business Media (2012)Google Scholar
  20. 20.
    Krohngrimberghe, A., Drumond, L., Freudenthaler, C., Schmidtthieme, L.: Multi-relational matrix factorization using bayesian personalized ranking for social network data, 173–182 (2012)Google Scholar
  21. 21.
    Li, Q., Li, J., Wang, H., Ginjala, A.: Semantics-enhanced privacy recommendation for social networking sites. In: IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp. 226–233 (2011)Google Scholar
  22. 22.
    Liu, K., Terzi, E.: A framework for computing the privacy scores of users in online social networks. Acm Trans. Knowl. Discov. Data 5(1), 1–30 (2010)CrossRefGoogle Scholar
  23. 23.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011. Hong Kong, China, February 9-12, 2011, pp. 287–296.  https://doi.org/10.1145/1935826.1935877 (2011)
  24. 24.
    Machanavajjhala, A., Korolova, A., Sarma, A.D.: Personalized social recommendations: Accurate or private. Proc. VLDB Endow. 4(7), 440–450 (2011)CrossRefGoogle Scholar
  25. 25.
    McSherry, F.: Privacy integrated queries: An extensible platform for privacy-preserving data analysis. Commun. ACM 53(9), 89–97 (2010).  https://doi.org/10.1145/1810891.1810916 CrossRefGoogle Scholar
  26. 26.
    Meng, X., Xu, Z., Chen, B., Zhang, Y.: Privacy-preserving query log sharing based on prior n-word aggregation. In: Trustcom, pp. 722–729 (2016)Google Scholar
  27. 27.
    Meng, X., Wang, S., Liu, H., Zhang, Y.: Exploiting emotion on reviews for recommender systems. In: AAAI (2018)Google Scholar
  28. 28.
    Minkus, T., Liu, K., Ross, K.W.: Children seen but not heard: When parents compromise children’s online privacy. In: International Conference on World Wide Web, pp. 776–786 (2015)Google Scholar
  29. 29.
    Nikolaenko, V., Ioannidis, S., Weinsberg, U., Joye, M., Taft, N., Boneh, D.: Privacy-preserving matrix factorization. In: 2013 ACM SIGSAC Conference on Computer and Communications Security, CCS’13. Berlin, Germany, November 4-8, 2013, pp. 801–812.  https://doi.org/10.1145/2508859.2516751 (2013)
  30. 30.
    Rajkumar, A., Agarwal, S.: A differentially private stochastic gradient descent algorithm for multiparty classification. Jmlr (2012)Google Scholar
  31. 31.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI 2009, pp. 452–461 (2009)Google Scholar
  32. 32.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada, December 3-6, 2007, pp. 1257–1264. http://papers.nips.cc/paper/3208-probabilistic-matrix-factorization (2007)
  33. 33.
    Shokri, R., Stronati, M., Shmatikov, V.: Membership inference attacks against machine learning models (2016)Google Scholar
  34. 34.
    Shu, K., Wang, S., Tang, J., Wang, Y., Liu, H.: Crossfire: Cross media joint friend and item recommendations. In: WSDM (2018)Google Scholar
  35. 35.
    Song, D., Meyer, D.A., Tao, D.: Top-k link recommendation in social networks. In: 2015 IEEE International Conference on Data Mining, ICDM 2015. Atlantic City, NJ, USA, November 14-17, 2015, pp. 389–398.  https://doi.org/10.1109/ICDM.2015.136 (2015)
  36. 36.
    Song, S., Wang, Y., Chaudhuri, K.: Pufferfish privacy mechanisms for correlated data. In: Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD Conference 2017. Chicago, IL, USA, May 14-19, 2017, pp. 1291–1306.  https://doi.org/10.1145/3035918.3064025 (2017)
  37. 37.
    Tang, J., Hu, X., Liu, H.: Social recommendation: A review. Social Netw. Analys. Mining 3(4), 1113–1133 (2013).  https://doi.org/10.1007/s13278-013-0141-9 CrossRefGoogle Scholar
  38. 38.
    Tang, Q., Wang, J.: Privacy-preserving friendship-based recommender systems. IEEE Trans. Depend. Secur. Comput. PP(99), 1–1 (2016)Google Scholar
  39. 39.
  40. 40.
    Wang, S., Tang, J., Liu, H.: Toward dual roles of users in recommender systems. In: CIKM (2015)Google Scholar
  41. 41.
    Wang, Y., Si, C., Wu, X.: Regression model fitting under differential privacy and model inversion attack. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pp. 1003–1009. http://ijcai.org/Abstract/15/146 (2015)
  42. 42.
    Wang, X., Lu, W., Ester, M., Wang, C., Chen, C.: Social recommendation with strong and weak ties. In: ACM International on Conference on Information and Knowledge Management, pp. 5–14 (2016)Google Scholar
  43. 43.
    Wang, S., Wang, Y., Tang, J., Shu, K., Ranganath, S., Liu, H.: What your images reveal: Exploiting visual contents for point-of-interest recommendation. In: Proceedings of WWW, pp. 391–400 (2017)Google Scholar
  44. 44.
    Wang, S., Tang, J., Wang, Y., Liu, H.: Exploring hierarchical structures for recommender systems. IEEE Transactions on Knowledge and Data Engineering (2018)Google Scholar
  45. 45.
    Xin, Y., Jaakkola, T.: Controlling privacy in recommender systems. In: Advances in Neural Information Processing Systems, pp. 2618–2626 (2014)Google Scholar
  46. 46.
    Ying, X., Wu, X., Wang, Y.: On linear refinement of differential privacy-preserving query answering. In: Advances in Knowledge Discovery and Data Mining, 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II, pp. 353–364 (2013)Google Scholar
  47. 47.
    Zhang, J., Zhang, Z., Xiao, X., Yang, Y., Winslett, M.: Functional mechanism: Regression analysis under differential privacy. Proc. Vldb Endow. 5(11), 1364–1375 (2012)CrossRefGoogle Scholar
  48. 48.
    Zhao, T., McAuley, J.J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014. Shanghai, China, November 3-7, 2014, pp. 261–270 (2014)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xuying Meng
    • 1
  • Suhang Wang
    • 2
  • Kai Shu
    • 2
  • Jundong Li
    • 2
  • Bo Chen
    • 3
  • Huan Liu
    • 2
  • Yujun Zhang
    • 1
    • 4
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Department of Computer ScienceArizona State UniversityTempeUSA
  3. 3.Department of Computer ScienceMichigan Technological UniversityHoughtonUSA
  4. 4.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations