Locality-Sensitive Hashing for Distributed Privacy-Preserving Collaborative Filtering: An Approach and System Architecture

  • Alexander SmirnovEmail author
  • Andrew Ponomarev
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 241)


Recommendation systems are currently widely used in domains where abundance of choice is conjoined with its subjective nature (books, movies, trips, etc.). Most of the modern recommendation systems are centralized. Although the centralized recommendation system design has some significant advantages, it also bears two primary disadvantages: the necessity for users to share their preferences and a single point of failure. This paper follows user-centric approach to distributed recommendation system design, and proposes an architecture of a collaborative peer-to-peer recommendation system with limited preferences’ disclosure. Privacy in the proposed design is provided by the fact that exact user preferences are never shared together with the user identity. To achieve that, the proposed architecture employs a locality-sensitive hashing of user preferences and an anonymized distributed hash table approach to peer-to-peer design.


Recommendation systems Distributed collaborative filtering Locality-sensitive hashing Peer-to-peer Anonymization Privacy 



The research was partially supported by projects funded by grants # 13-07-00271, # 13-07-00039, and # 14-07-00345 of the Russian Foundation for Basic Research, project 213 (program 8) of the Presidium of the Russian Academy of Sciences, project # 2.2 of the basic research program “Intelligent information technologies, system analysis and automation” of the Nanotechnology and Information technology Department of the Russian Academy of Sciences. This work was partially financially supported by the Government of the Russian Federation, Grant 074-U01.


  1. 1.
    Amatriain, X., Jaimes, A., Oliver, N., Pujol, J.M.: Data mining methods for recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook. Springer, Heidelberg (2011)Google Scholar
  2. 2.
    Bakker, A., Ogston, E., van Steen, M.: Collaborative filtering using random neighbours in peer-to-peer networks. In: Workshop on Complex Networks in Information and Knowledge Management, pp. 67–75 (2009)Google Scholar
  3. 3.
    Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: STOC 2002 Proceedings of the 34th Annual ACM Symposium on Theory of Computing, pp. 380–388 (2002)Google Scholar
  4. 4.
    Chen, X., et al.: SCOPE: scalable consistency maintenance in structured P2P systems. In: Proceedings of IEEE INFOCOM 2005, pp. 1502–1513 (2005)Google Scholar
  5. 5.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys 2010), pp. 39–46. ACM, New York, NY, USA (2010)Google Scholar
  6. 6.
    Datar, M., et al.: Locality-sensitive hashing scheme based on p-Stable distributions. In: SCG 2004 Proceedings of the 20th Annual Symposium on Computational Geometry, pp. 253–262 (2004)Google Scholar
  7. 7.
    Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook. Springer, Heidelberg (2011)Google Scholar
  8. 8.
    Draidi, F., Pacitti, E., Kemme, B.: P2Prec: a P2P recommendation system for large-scale data sharing. J. Trans. Large-Scale Data Knowl.-Centered Syst. (TLDKS) 3, 87–116 (2011)Google Scholar
  9. 9.
    Draidi, F., et al.: P2Prec: a social-based P2P recommendation system. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2593–2596 (2011)Google Scholar
  10. 10.
    Han, P., et al.: A scalable P2P recommendation system based on distributed collaborative filtering. Expert Syst. Appl. 27(2), 203–210 (2004)CrossRefGoogle Scholar
  11. 11.
    Hecht, F., et al.: Radiommendation: P2P on-line radio with a distributed recommendation system. In: Proceedings of the IEEE 12th International Conference on Peer-to-Peer Computing, pp. 73–74 (2012)Google Scholar
  12. 12.
    Hu, Y., Bhuyan, L.N., Feng, M.: Maintaining data consistency in structured P2P systems. IEEE Trans. Parallel Distrib. Syst. 23(11), 2125–2137 (2012)CrossRefGoogle Scholar
  13. 13.
    Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: STOC 1998 Proceedings of the 30th Symposium on Theory of Computing, pp. 604–613 (1998)Google Scholar
  14. 14.
    Jelasity, M., Montresor, A., Babaoglu, O.: T-Man: gossip-based fast overlay topology construction. Comput. Netw. 53(13), 2321–2339 (2009)CrossRefzbMATHGoogle Scholar
  15. 15.
    Kermarrec, A.-M., et al.: Application of random walks to decentralized recommendation systems. In: Proceeding of the 14th International Conference on Principles of Distributed Systems, pp. 48–63 (2010)Google Scholar
  16. 16.
    Korzun, D., Gurtov, A.: Structured Peer-to-Peer Systems. Fundamentals of Hierarchical Organization, Routing, Scaling and Security. Springer, Heidelberg (2013)CrossRefzbMATHGoogle Scholar
  17. 17.
    Mastroianni, C., Pirro, G., Talia, D.: Data consistency and peer synchronization in cooperative P2P environments. Technical report (2008, unpublished)Google Scholar
  18. 18.
    Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8(1), 3–30 (1998)CrossRefzbMATHGoogle Scholar
  19. 19.
  20. 20.
    Jelasity, M., Hegedűs, I., Ormándi, R.: Overlay management for fully distributed user-based collaborative filtering. In: D’Ambra, P., Guarracino, M., Talia, D. (eds.) Euro-Par 2010, Part I. LNCS, vol. 6271, pp. 446–457. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Oster, G., et al.: Data consistency for P2P collaborative editing. In: Proceedings of the 20th Anniversary Conference on Computer Supported Cooperative Work, pp. 259–268 (2006)Google Scholar
  22. 22.
    Pitsilis, G., Marshall, L.: A trust-enabled P2P recommendation system. In: Proceedings of 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 59–64 (2006)Google Scholar
  23. 23.
    Pussep, K., et al.: A peer-to-peer recommendation system with privacy constraints. In: CISIS: IEEE Computer Society, pp. 409–414 (2009)Google Scholar
  24. 24.
    Rajaraman, A., Ullman, J.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2012)Google Scholar
  25. 25.
    Slanley, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbors. IEEE Signal Process. Mag. 25(2), 128–131 (2008)CrossRefGoogle Scholar
  26. 26.
    Tveit, A.: Peer-to-peer based recommendations for mobile commerce. In: Proceedings of 1st International Workshop on Mobile Commerce (WMC 2001), pp. 26–29. ACM (2001)Google Scholar
  27. 27.
    Wang, Q., Borisov, N.: Octopus: a secure and anonymous DHT lookup. In: Proceedings of the IEEE 32nd International Conference on Distributed Computing Systems, pp. 325–334 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.St. Petersburg Institute for Informatics and Automation of the RASSt. PetersburgRussia
  2. 2.ITMO UniversitySt. PetersburgRussia

Personalised recommendations