Advertisement

Privacy-Preserving Distributed Collaborative Filtering

  • Antoine BoutetEmail author
  • Davide Frey
  • Rachid Guerraoui
  • Arnaud Jégou
  • Anne-Marie Kermarrec
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8593)

Abstract

We propose a new mechanism to preserve privacy while leveraging user profiles in distributed recommender systems. Our mechanism relies on (i) an original obfuscation scheme to hide the exact profiles of users without significantly decreasing their utility, as well as on (ii) a randomized dissemination protocol ensuring differential privacy during the dissemination process.

We compare our mechanism with a non-private as well as with a fully private alternative. We consider a real dataset from a user survey and report on simulations as well as planetlab experiments. We dissect our results in terms of accuracy and privacy trade-offs, bandwidth consumption, as well as resilience to a censorship attack. In short, our extensive evaluation shows that our twofold mechanism provides a good trade-off between privacy and accuracy, with little overhead and high resilience.

Keywords

User Profile Target Node Malicious Node Collaborative Filter News Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: PODS (2001)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: SIGMOD (2000)Google Scholar
  3. 3.
    Ahmad, W., Khokhar, A.: An architecture for privacy preserving collaborative filtering on web portals. In: IAS (2007)Google Scholar
  4. 4.
    Alaggan, M., Gambs, S., Kermarrec, A.-M.: BLIP: non-interactive differentially-private similarity computation on bloom filters. In: Richa, A.W., Scheideler, C. (eds.) SSS 2012. LNCS, vol. 7596, pp. 202–216. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  5. 5.
    Boutet, A., Frey, D., Guerraoui, R., Jégou, A., Kermarrec, A-.M.: Privacy-preserving distributed collaborative filtering. Technical report RR-8253, INRIA, March 2013Google Scholar
  6. 6.
    Boutet, A., Frey, D., Guerraoui, R., Jégou, A., Kermarrec, A.-M.: WhatsUp Decentralized Instant News Recommender. In: IPDPS (2013)Google Scholar
  7. 7.
    Canny, J.: Collaborative filtering with privacy via factor analysis. In: SIGIR (2002)Google Scholar
  8. 8.
    Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW (2007)Google Scholar
  9. 9.
    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
  10. 10.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  11. 11.
    Goldreich, O.: Cryptography and cryptographic protocols. Distrib. Comput. 16, 177–199 (2003)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Haeberlen, A., Pierce, B.C., Narayan, A.: Differential privacy under fire. In: SEC (2011)Google Scholar
  13. 13.
    Huang, Z., Du, W., Chen, B.: Deriving private information from randomized data. In: SIGMOD (2005)Google Scholar
  14. 14.
    Kanerva, P., Kristoferson, J., Holst, A.: Random indexing of text samples for latent semantic analysis. In: CCSS (2000)Google Scholar
  15. 15.
    Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: ICDM (2003)Google Scholar
  16. 16.
    Machanavajjhala, A., Korolova, A., Sarma,A.D.: Personalized social recommendations: accurate or private. In: VLDB (2011)Google Scholar
  17. 17.
    Polat, H., Du, W.: Svd-based collaborative filtering with privacy. In: SAC (2005)Google Scholar
  18. 18.
    Singh, A., Castro, M., Druschel, P., Rowstron, A.: Defending against eclipse attacks on overlay networks. In: SIGOPS (2004)Google Scholar
  19. 19.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. (2009)Google Scholar
  20. 20.
    Tarkoma, S., Rothenberg, C.E., Lagerspetz, E.: Theory and practice of bloom filters for distributed systems. IEEE Commun. Surv. Tutorials 14, 131–155 (2012)CrossRefGoogle Scholar
  21. 21.
    van Rijsbergen, C.J.: Information Retrieval. Butterworth, London (1979)Google Scholar
  22. 22.
    Voulgaris, S., Gavidia, D., van Steen, M.: Cyclon: inexpensive membership management for unstructured p2p overlays. J. Netw. Syst. Manage. 13, 197–217 (2005)CrossRefGoogle Scholar
  23. 23.
    Voulgaris, S., van Steen, M.: Epidemic-style management of semantic overlays for content-based searching. In: Cunha, J.C., Medeiros, P.D. (eds.) Euro-Par 2005. LNCS, vol. 3648, pp. 1143–1152. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  24. 24.
    Wan, M., Jönsson, A., Wang, C., Li, L., Yang, Y.: A random indexing approach for web user clustering and web prefetching. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds.) PAKDD Workshops 2011. LNCS, vol. 7104, pp. 40–52. Springer, Heidelberg (2012) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Antoine Boutet
    • 1
  • Davide Frey
    • 1
  • Rachid Guerraoui
    • 2
  • Arnaud Jégou
    • 1
  • Anne-Marie Kermarrec
    • 1
  1. 1.INRIA RennesRennesFrance
  2. 2.EPFLLausanneSwitzerland

Personalised recommendations