An Enhanced Privacy-Preserving Recommender System

  • Pranav VermaEmail author
  • Harshul Vaishnav
  • Anish Mathuria
  • Sourish Dasgupta
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 939)


A recommender system stores historical data collected over a long period from various users, these are used to predict how new and existing users would rate an item. As user data is stored by the system, this poses threat to user’s privacy. The goal of a privacy preserving recommender system is to hide user ratings from system and yet allow to make recommendations.

A recent example is the privacy-preserving recommender scheme proposed by Badsha, Yi and Khalil. Their scheme assumes that the server is semi-honest. However, when the server is malicious an attack is possible, as shown by Mu, Shao and Miglani. In this paper, we propose a simple modification to their scheme, which preserves the privacy of ratings against a malicious server. We demonstrate that the computation and communication costs of modified protocol are reasonable in comparison to original protocol.


Recommender system Privacy Collaborative Filtering Content Based Filtering Homomorphic encryption 


  1. 1.
    Badsha, S., Yi, X., Khalil, I.: A practical privacy-preserving recommender system. Data Sci. Eng. 1(3), 161–177 (2016)CrossRefGoogle Scholar
  2. 2.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  3. 3.
    Canny, J.: Collaborative filtering with privacy. In: 2002 Proceedings of the IEEE Symposium on Security and Privacy, pp. 45–57. IEEE (2002)Google Scholar
  4. 4.
    Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Boston (2011). Scholar
  5. 5.
    ElGamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31(4), 469–472 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Mu, E., Shao, C., Miglani, V.: Privacy preserving collaborative filtering (2017).
  7. 7.
    Gentry, C., Boneh, D.: A fully homomorphic encryption scheme, vol. 20. Stanford University, Stanford (2009)Google Scholar
  8. 8.
    Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., Wernsing, J.: CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning, pp. 201–210 (2016)Google Scholar
  9. 9.
    Kikuchi, H., Kizawa, H., Tada, M.: Privacy-preserving collaborative filtering schemes. In: 2009 International Conference on Availability, Reliability and Security, ARES 2009, pp. 911–916. IEEE (2009)Google Scholar
  10. 10.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer (8), 30–37 (2009)CrossRefGoogle Scholar
  11. 11.
    Machanavajjhala, A., Korolova, A., Sarma, A.D.: Personalized social recommendations: accurate or private. Proc. VLDB Endow. 4(7), 440–450 (2011)CrossRefGoogle Scholar
  12. 12.
    Marlin, B.: Collaborative filtering: a machine learning perspective. University of Toronto (2004)Google Scholar
  13. 13.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). Scholar
  14. 14.
    Polat, H., Du, W.: Privacy-preserving collaborative filtering using randomized perturbation techniques. In: 2003 Third IEEE International Conference on Data Mining, ICDM 2003, pp. 625–628. IEEE (2003)Google Scholar
  15. 15.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994)Google Scholar
  16. 16.
    Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 1–34. Springer, Boston (2015). Scholar
  17. 17.
    Shmueli, E., Tassa, T.: Secure multi-party protocols for item-based collaborative filtering. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 89–97. ACM (2017)Google Scholar
  18. 18.
    Tada, M., Kikuchi, H., Puntheeranurak, S.: Privacy-preserving collaborative filtering protocol based on similarity between items. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 573–578. IEEE (2010)Google Scholar
  19. 19.
    Wang, J., Arriaga, A., Tang, Q., Ryan, P.Y.A.: CryptoRec: secure recommendations as a service. CoRR abs/1802.02432 (2018).
  20. 20.
    Yakut, I., Polat, H.: Arbitrarily distributed data-based recommendations with privacy. Data Knowl. Eng. 72, 239–256 (2012)CrossRefGoogle Scholar
  21. 21.
    Zhong, G., Goldberg, I., Hengartner, U.: Louis, lester and pierre: three protocols for location privacy. In: Borisov, N., Golle, P. (eds.) PET 2007. LNCS, vol. 4776, pp. 62–76. Springer, Heidelberg (2007). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Pranav Verma
    • 1
    Email author
  • Harshul Vaishnav
    • 1
  • Anish Mathuria
    • 1
  • Sourish Dasgupta
    • 1
  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia

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