Privacy Preserving Collaborative Filtering from Asymmetric Randomized Encoding

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8975)

Abstract

Collaborative filtering is a famous technique in recommendation systems. Yet, it requires the users to reveal their preferences, which has undesirable privacy implications. Over the years, researchers have proposed many privacy-preserving collaborative filtering (PPCF) systems using very different techniques for different settings, ranging from adding noise to the data with centralized filtering, to performing secure multi-party computation. However, either privacy protection is unsatisfactory or the computation is prohibitively expensive.

In this work, we propose a decentralized PPCF system, which enables a group of users holding (cryptographically low-entropy) profile to identify other similar users in a privacy-preserving yet very efficient way, without the help of any central server. Its core component is a novel primitive which we named as asymmetric randomized encoding (ARE). Similar to the spirt of other cryptographic primitives, it is asymmetric in the sense that, honest party could enjoy performance boost (via precomputation) with the knowledge of a profile, whilst adversary aiming to recover the hidden profile can only launch dictionary attack against each encoded profile. Thanks to the simple design of ARE, our solution is very efficient, which is demonstrated by our performance evaluation. Besides PPCF, we believe that ARE will find further applications which require a balance between privacy and efficiency.

Keywords

Asymmetric randomized encoding Privacy-preserving collaborative filtering Recommendation system Peer-to-peer network 

References

  1. 1.
    Ahmad, W., Khokhar, A.A.: An architecture for privacy preserving collaborative filtering on web portals. In: IAS, pp. 273–278. IEEE Computer Society (2007)Google Scholar
  2. 2.
    Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51(1), 117–122 (2008)CrossRefGoogle Scholar
  3. 3.
    Androutsellis-Theotokis, S., Spinellis, D.: A survey of peer-to-peer content distribution technologies. ACM Comput. Surv. 36(4), 335–371 (2004)CrossRefGoogle Scholar
  4. 4.
    Basu, A., Vaidya, J., Kikuchi, H., Dimitrakos, T.: Privacy-preserving collaborative filtering for the cloud. In: Lambrinoudakis, C., Rizomiliotis, P., Wlodarczyk, T.W. (eds.) CloudCom, pp. 223–230. IEEE (2011)Google Scholar
  5. 5.
    Bellare, M., Boldyreva, A., O’Neill, A.: Deterministic and efficiently searchable encryption. In: Menezes, A. (ed.) CRYPTO 2007. LNCS, vol. 4622, pp. 535–552. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  6. 6.
    Bellare, M., Pointcheval, D., Rogaway, P.: Authenticated key exchange secure against dictionary attacks. In: Preneel, B. (ed.) EUROCRYPT 2000. LNCS, vol. 1807, pp. 139–155. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  7. 7.
    Bellare, M., Rogaway, P.: Random oracles are practical: a paradigm for designing efficient protocols. In: Denning, D.E., Pyle, R., Ganesan, R., Sandhu, R.S., Ashby, V. (eds.) ACM Conference on Computer and Communications Security, pp. 62–73. ACM (1993)Google Scholar
  8. 8.
    Berkovsky, S., Eytani, Y., Kuflik, T., Ricci, F.: Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Konstan, J.A., Riedl, J., Smyth, B. (eds.) RecSys, pp. 9–16. ACM (2007)Google Scholar
  9. 9.
    Bertier, M., Frey, D., Guerraoui, R., Kermarrec, A.-M., Leroy, V.: The Gossple anonymous social network. In: Gupta, I., Mascolo, C. (eds.) Middleware 2010. LNCS, vol. 6452, pp. 191–211. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  10. 10.
    Canard, S., Fuchsbauer, G., Gouget, A., Laguillaumie, F.: Plaintext-Checkable Encryption. In: Dunkelman, O. (ed.) CT-RSA 2012. LNCS, vol. 7178, pp. 332–348. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  11. 11.
    Canny, J.F.: Collaborative filtering with privacy. In: IEEE Symposium on Security and Privacy, pp. 45–57. IEEE Computer Society (2002)Google Scholar
  12. 12.
    Charikar, M.: Similarity estimation techniques from rounding algorithms. In: Reif, J.H. (ed.) STOC, pp. 380–388. ACM (2002)Google Scholar
  13. 13.
    Chow, R., Pathak, M.A., Wang, C.: A practical system for privacy-preserving collaborative filtering. In: Vreeken, J., Ling, C., Zaki, M.J., Siebes, A., Yu, J.X., Goethals, B., Webb, G.I., Wu, X. (eds.) ICDM Workshops, pp. 547–554. IEEE Computer Society (2012)Google Scholar
  14. 14.
    Damgård, I., Hofheinz, D., Kiltz, E., Thorbek, R.: Public-key encryption with non-interactive opening. In: Malkin, T. (ed.) CT-RSA 2008. LNCS, vol. 4964, pp. 239–255. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  15. 15.
    Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  16. 16.
    Galindo, D., Libert, B., Fischlin, M., Fuchsbauer, G., Lehmann, A., Manulis, M., Schröder, D.: Public-key encryption with non-interactive opening: new constructions and stronger definitions. In: Bernstein, D.J., Lange, T. (eds.) AFRICACRYPT 2010. LNCS, vol. 6055, pp. 333–350. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  17. 17.
    Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Mitzenmacher, M. (ed.) STOC, pp. 169–178. ACM (2009)Google Scholar
  18. 18.
    Gentry, C., Halevi, S., Smart, N.P.: Fully homomorphic encryption with polylog overhead. In: Pointcheval, D., Johansson, T. (eds.) EUROCRYPT 2012. LNCS, vol. 7237, pp. 465–482. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  19. 19.
    Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game or a completeness theorem for protocols with honest majority. In: Aho, A.V. (ed.) STOC, pp. 218–229. ACM (1987)Google Scholar
  20. 20.
    Goldwasser, S., Micali, S.: Probabilistic encryption. J. Comput. Syst. Sci. 28(2), 270–299 (1984)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Grigorik, I.: Dissecting the Netflix Dataset - igvita.com. Last accessed on 2014–09-12Google Scholar
  22. 22.
    Hu, P., Chow, S.S.M, Lau, W.C.: Secure friend discovery via privacy-preserving and decentralized community detection. In: ICML 2014 Workshop on Learning, Security and Privacy (2014). Full version appears at http://arxiv.org/abs/1405.4951
  23. 23.
    Huang, Z., Du, W., Chen, B.: Deriving private information from randomized data. In: Özcan, F. (ed.) SIGMOD Conference, pp. 37–48. ACM (2005)Google Scholar
  24. 24.
    Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: [45], pp. 99–106Google Scholar
  25. 25.
    Katz, J., Ostrovsky, R., Yung, M.: Efficient password-authenticated key exchange using human-memorable passwords. In: Pfitzmann, B. (ed.) EUROCRYPT 2001. LNCS, vol. 2045, pp. 475–494. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  26. 26.
    Katz, J., Ostrovsky, R., Yung, M.: Efficient and secure authenticated key exchange using weak passwords. J. ACM, 57(1) (2009)Google Scholar
  27. 27.
    Liu, J.K., Baek, J., Zhou, J., Yang, Y., Wong, J.W.: Efficient online/offline identity-based signature for wireless sensor network. Int. J. Inf. Sec. 9(4), 287–296 (2010)CrossRefMATHGoogle Scholar
  28. 28.
    McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the netflix prize contenders. In: IV, J.F.E., Fogelman-Soulié, F., Flach, P.A., Zaki, M.J. (eds.) KDD, pp. 627–636. ACM (2009)Google Scholar
  29. 29.
    Nandi, A., Aghasaryan, A., Bouzid, M.: P3: a privacy preserving personalization middleware for recommendation-based services. In: Hot Topics in Privacy Enhancing Technologies Symposium (2011)Google Scholar
  30. 30.
    Nandi, A., Aghasaryan, A., Chhabra, I.: On the use of decentralization to enable privacy in web-scale recommendation services. In: [35], pp. 25–36Google Scholar
  31. 31.
    Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: IEEE Symposium on Security and Privacy, pp. 111–125. IEEE Computer Society (2008)Google Scholar
  32. 32.
    Parra-Arnau, J., Rebollo-Monedero, D., Forné, J.: A privacy-protecting architecture for collaborative filtering via forgery and suppression of ratings. In: Garcia-Alfaro, J., Navarro-Arribas, G., Cuppens-Boulahia, N., de Capitani di Vimercati, S. (eds.) DPM 2011 and SETOP 2011. LNCS, vol. 7122, pp. 42–57. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  33. 33.
    Polat, H., Du, W.: Privacy-preserving collaborative filtering using randomized perturbation techniques. In: [45], pp. 625–628Google Scholar
  34. 34.
    Polat, H., Du, W.: Achieving private recommendations using randomized response techniques. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 637–646. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  35. 35.
    Sadeghi, A., Foresti, S. (eds.) Proceedings of the 12th annual ACM Workshop on Privacy in the Electronic Society, WPES 2013, Berlin, Germany, November 4, 2013. ACM (2013)Google Scholar
  36. 36.
    Shin, J.S., Gligor, V.D.: A New Privacy-Enhanced Matchmaking Protocol. IEICE Trans. 96–B(8), 2049–2059 (2013). Preliminary version appeared at NDSS 2008CrossRefGoogle Scholar
  37. 37.
    Shokri, R., Pedarsani, P., Theodorakopoulos, G., Hubaux, J.-P.: Preserving privacy in collaborative filtering through distributed aggregation of offline profiles. In: Bergman, L.D., Tuzhilin, A., Burke, R.D., Felfernig, A., Schmidt-Thieme, L. (eds) RecSys, pp. 157–164. ACM (2009)Google Scholar
  38. 38.
    Tang, Q.: Public key encryption schemes supporting equality test with authorisation of different granularity. IJACT 2(4), 304–321 (2012)MathSciNetCrossRefMATHGoogle Scholar
  39. 39.
    Tang, Q.: Public key encryption supporting plaintext equality test and user-specified authorization. Secur. Commun. Netw. 5(12), 1351–1362 (2012)CrossRefGoogle Scholar
  40. 40.
    Tran, D.N., Li, J., Subramanian, L., Chow, S.S.M.: Optimal sybil-resilient node admission control. In: INFOCOM, pp. 3218–3226. IEEE (2011)Google Scholar
  41. 41.
    van Dijk, M., Gentry, C., Halevi, S., Vaikuntanathan, V.: Fully homomorphic encryption over the integers. In: Gilbert, H. (ed.) EUROCRYPT 2010. LNCS, vol. 6110, pp. 24–43. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  42. 42.
    Wikipedia. Collaborative Filtering (2014). http://en.wikipedia.org/wiki/Collaborative_filtering. Last accessed on 2014–09-12
  43. 43.
    Wikipedia. Netflix Prize (2014). http://en.wikipedia.org/wiki/Netflix_Prize. Last accessed on 2014–09-12
  44. 44.
    Wu, T.-S., Lin, H.-Y.: Non-interactive authenticated key agreement over the mobile communication network. MONET 18(5), 594–599 (2013)MathSciNetGoogle Scholar
  45. 45.
    Wu, X., Tuzhilin, A., Shavlik, J. (eds.) Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 19–22 December 2003, Melbourne, Florida, USA. IEEE Computer Society (2003)Google Scholar
  46. 46.
    Yang, G., Tan, C.H., Huang, Q., Wong, D.S.: Probabilistic public key encryption with equality test. In: Pieprzyk, J. (ed.) CT-RSA 2010. LNCS, vol. 5985, pp. 119–131. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  47. 47.
    Yao, A.C.-C.: How to generate and exchange secrets (Extended Abstract). In: FOCS, pp. 162–167. IEEE Computer Society (1986)Google Scholar
  48. 48.
    Zhang, S., Ford, J., Makedon, F.: Deriving private information from randomly perturbed ratings. In: Ghosh, J., Lambert, D., Skillicorn, D.B., Srivastava, J. (eds) SDM, pp. 59–69. SIAM (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Information EngineeringThe Chinese University of Hong KongHong KongHong Kong SAR

Personalised recommendations