Skip to main content
SpringerLink
Log in
Book cover

International Conference on Enterprise Information Systems

ICEIS 2015: Enterprise Information Systems pp 455–475Cite as

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

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 241))

Abstract

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  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. 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. 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. 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. 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. 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. 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. 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. 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. Han, P., et al.: A scalable P2P recommendation system based on distributed collaborative filtering. Expert Syst. Appl. 27(2), 203–210 (2004)

    Article  Google Scholar 

  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. Hu, Y., Bhuyan, L.N., Feng, M.: Maintaining data consistency in structured P2P systems. IEEE Trans. Parallel Distrib. Syst. 23(11), 2125–2137 (2012)

    Article  Google Scholar 

  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. Jelasity, M., Montresor, A., Babaoglu, O.: T-Man: gossip-based fast overlay topology construction. Comput. Netw. 53(13), 2321–2339 (2009)

    Article  MATH  Google Scholar 

  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. Korzun, D., Gurtov, A.: Structured Peer-to-Peer Systems. Fundamentals of Hierarchical Organization, Routing, Scaling and Security. Springer, Heidelberg (2013)

    Book  MATH  Google Scholar 

  17. Mastroianni, C., Pirro, G., Talia, D.: Data consistency and peer synchronization in cooperative P2P environments. Technical report (2008, unpublished)

    Google Scholar 

  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)

    Article  MATH  Google Scholar 

  19. Netflix Prize. http://www.netflixprize.com/

  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)

    Chapter  Google Scholar 

  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. 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. 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. Rajaraman, A., Ullman, J.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2012)

    Google Scholar 

  25. Slanley, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbors. IEEE Signal Process. Mag. 25(2), 128–131 (2008)

    Article  Google Scholar 

  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. 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 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation of the RAS, 14th line, 39, St. Petersburg, Russia

    Alexander Smirnov & Andrew Ponomarev

  2. ITMO University, Kronverksky, 49, St. Petersburg, Russia

    Alexander Smirnov

Authors
  1. Alexander Smirnov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Ponomarev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Smirnov .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Macquarie University, Sydney, Wroclaw University of Economics, Poland, Wroclaw, Poland

    Leszek Maciaszek

  3. Despatx Omega-132, Polytechnic University of Catalonia, Barcelona, Spain

    Ernest Teniente

  4. MODESTE/ESEO, Angers, France

    Olivier Camp

  5. INSTICC, Instituto Politécnico de Setúbal, Setúbal, Portugal

    José Cordeiro

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Smirnov, A., Ponomarev, A. (2015). Locality-Sensitive Hashing for Distributed Privacy-Preserving Collaborative Filtering: An Approach and System Architecture. In: Hammoudi, S., Maciaszek, L., Teniente, E., Camp, O., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2015. Lecture Notes in Business Information Processing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-29133-8_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-29133-8_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29132-1

  • Online ISBN: 978-3-319-29133-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation