Advertisement

Multimedia Tools and Applications

, Volume 64, Issue 2, pp 249–275 | Cite as

Multi-agent based middleware for protecting privacy in IPTV content recommender services

  • Ahmed M. Elmisery
  • Dmitri Botvich
Article

Abstract

This work presents our efforts to design an agent based middleware that enables the end-users to use IPTV content recommender services without revealing their sensitive preference data to the service provider or any third party involved in this process. The proposed middleware (called AMPR) preserves users’ privacy when using the recommender service and permits private sharing of data among different users in the network. The proposed solution relies on a distributed multi-agent architecture involving local agents running on the end-user set up box to implement a two stage concealment process based on user role in order to conceal the local preference data of end-users when they decide to participate in recommendation process. Moreover, AMPR allows the end-users to use P3P policies exchange language (APPEL) for specifying their privacy preferences for the data extracted from their profiles, while the recommender service uses platform for privacy preferences (P3P) policies for specifying their data usage practices. AMPR executes the first stage locally at the end user side but the second stage is done at remote nodes that can be donated by multiple non-colluding end users that we will call super-peers Elmisery and Botvich (2011a, b, c); or third parties mash-up service Elmisery A, Botvich (2011a, b). Participants submit their locally obfuscated profiles anonymously to their local super-peer who collect and mix these preference data from multiple participants. The super-peer invokes AMPR to perform global perturbation process on the aggregated preference data to ensure a complete concealment of user’s profiles. Then, it anonymously submits these aggregated profiles to a third party content recommender service to generate referrals without breaching participants’ privacy. In this paper, we also provide an IPTV network scenario and experimentation results. Our results and analysis shows that our two-stage concealment process not only protect the users’ privacy, but also can maintain the recommendation accuracy

Keywords

Privacy Clustering IPTV networks Recommender System Multi-agent 

Notes

Acknowledgments

This work has received support from the Higher Education Authority in Ireland under the PRTLI Cycle 4 programme, in the FutureComm project (Serving Society: Management of Future Communications Networks and Services).

References

  1. 1.
    Canny J (2002) Collaborative filtering with privacy via factor analysis. Paper presented at the Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, Tampere, FinlandGoogle Scholar
  2. 2.
    Canny J (2002) Collaborative filtering with privacy. Paper presented at the Proceedings of the 2002 IEEE Symposium on Security and PrivacyGoogle Scholar
  3. 3.
    Carbo J, Molina J, Davila J (2002) Trust management through fuzzy reputation. Int J Cooper Inform Syst 12:135–155CrossRefGoogle Scholar
  4. 4.
    Dingledine R, Mathewson N, Syverson P (2004) Tor: the second-generation onion router. Paper presented at the Proceedings of the 13th conference on USENIX Security Symposium - Volume 13, San Diego, CAGoogle Scholar
  5. 5.
    Elmisery A, Botvich D (2011) Private recommendation service ror IPTV system. In: 12th IFIP/IEEE International Symposium on Integrated Network Management, Dublin, Ireland. IEEEGoogle Scholar
  6. 6.
    Elmisery A, Botvich D (2011) Privacy aware recommender service for IPTV networks. In: 5th FTRA/IEEE International Conference on Multimedia and Ubiquitous Engineering, Crete, Greece. IEEEGoogle Scholar
  7. 7.
    Elmisery A, Botvich D (2011) Agent based middleware for maintaining user privacy in IPTV recommender services. In: 3rd International ICST Conference on Security and Privacy in Mobile Information and Communication Systems. ICST, Aalborg, DenmarkGoogle Scholar
  8. 8.
    Elmisery A, Botvich D (2011) Agent based middleware for private data mashup in IPTV recommender services. In: 16th IEEE International Workshop on Computer Aided Modeling, Analysis and Design of Communication Links and Networks. IEEE, Kyoto, JapanGoogle Scholar
  9. 9.
    Elmisery A, Botvich (2011) D An agent based middleware for privacy aware recommender systems in IPTV Networks. In: 3rd International Conference on Intelligent Decision Technologies University of Piraeus, Greece, KES-Springer Smart Innovations, Systems and technologies. Springer VerlagGoogle Scholar
  10. 10.
    Elmisery AM, Botvich D (2011) An agent based middleware for privacy aware recommender systems in IPTV networks. In: Watada J, Phillips-Wren G, Jain LC, Howlett RJ (eds) Intelligent decision technologies. Smart innovation, systems and technologies, vol 10. Springer, Berlin, Heidelberg, pp 821–832. doi: 10.1007/978-3-642-22194-1_81 CrossRefGoogle Scholar
  11. 11.
    Elmisery A, Botvich D (2011) Privacy aware obfuscation middleware for mobile jukebox recommender services. In: The 11th IFIP Conference on e-Business, e-Service, e-Society, Kaunas, Lithuania, IFIPGoogle Scholar
  12. 12.
    Elmisery A, Botvich D (2011) Enhanced Middleware for Collaborative Privacy in IPTV Recommender Services Journal of Convergence 2 (2):10Google Scholar
  13. 13.
    Elmisery A, Huaiguo F (2010) Privacy preserving distributed learning clustering of healthcare data using cryptography protocols. In: 34th IEEE Annual International Computer Software and Applications Workshops, Seoul, South KoreaGoogle Scholar
  14. 14.
    Esma A (2008) Experimental demonstration of a hybrid privacy-preserving recommender system. In: Gilles B, Jose MF, Flavien Serge Mani O, Zbigniew R (eds) pp 161–170Google Scholar
  15. 15.
    Fellows MR, Guo J, Komusiewicz C, Niedermeier R, Uhlmann J (2009) Graph-Based Data Clustering with Overlaps. Paper presented at the Proceedings of the 15th Annual International Conference on Computing and Combinatorics, Niagara Falls, NYGoogle Scholar
  16. 16.
    Gemmis Md, Iaquinta L, Lops P, Musto C, Narducci F, Semeraro G (2009) Preference Learning in recommender systems. Paper presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), SloveniaGoogle Scholar
  17. 17.
    Ghinita G, Kalnis P, Skiadopoulos S (2007) PRIVE: anonymous location-based queries in distributed mobile systems. Paper presented at the Proceedings of the 16th international conference on World Wide Web, Banff, Alberta, CanadaGoogle Scholar
  18. 18.
    Gupta D, Digiovanni M, Narita H, Goldberg K (1999) Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm. Paper presented at the Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, Berkeley, California, United StatesGoogle Scholar
  19. 19.
    Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53. doi: doi.acm.org/10.1145/963770.963772 CrossRefGoogle Scholar
  20. 20.
    Huang Z, Du W, Chen B (2005) Deriving private information from randomized data. Paper presented at the Proceedings of the 2005 ACM SIGMOD international conference on Management of data, Baltimore, MarylandGoogle Scholar
  21. 21.
    Imani M, Taheri M, Naderi M (2010) Security enhanced routing protocol for ad hoc networks. J Conv 1(1):43–48Google Scholar
  22. 22.
    Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. Paper presented at the Proceedings of the thirtieth annual ACM symposium on theory of computing, Dallas, Texas, United StatesGoogle Scholar
  23. 23.
    Jarvis RA, Patrick EA (1973) Clustering using a similarity measure based on shared near neighbors. IEEE Trans Comput 22(11):1025–1034. doi: 10.1109/t-c.1973.223640 CrossRefGoogle Scholar
  24. 24.
    Kargupta H, Datta S, Wang Q, Sivakumar K (2003) On the privacy preserving properties of random data perturbation techniques. Paper presented at the Proceedings of the Third IEEE International Conference on Data MiningGoogle Scholar
  25. 25.
    Kelly D, Teevan J (2003) Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37(2):18–28. doi: doi.acm.org/10.1145/959258.959260 CrossRefGoogle Scholar
  26. 26.
    Kingsford C (2009) Information theory notesGoogle Scholar
  27. 27.
    Klyuev V, Oleshchuk V (2011) Semantic retrieval: an approach to representing, searching and summarising text documents. Int J Inf Technol Commun Converg 1:221–234Google Scholar
  28. 28.
    Konstan J, Miller B, Maltz D, Herlocker J, Gordon L, Riedl J (1997) GroupLens: applying collaborative filtering to usenet news. Commun ACM 40(3):77–87. doi: citeulike-article-id:486168 CrossRefGoogle Scholar
  29. 29.
    Liu K, Giannella C, Kargupta H (2006) An attacker’s view of distance preserving maps for privacy preserving data mining knowledge discovery in databases: PKDD 2006. In: Fürnkranz J, Scheffer T, Spiliopoulou M (eds) Lecture notes in computer science vol 4213. Springer, Berlin / Heidelberg, pp 297–308. doi: 10.1007/11871637_30 Google Scholar
  30. 30.
    McSherry F, Mironov I (2009) Differentially private recommender systems: building privacy into the net. Paper presented at the Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Paris, FranceGoogle Scholar
  31. 31.
    Miller BN, Konstan JA, Riedl J (2004) PocketLens: toward a personal recommender system. ACM Trans Inf Syst 22(3):437–476. doi: doi.acm.org/10.1145/1010614.1010618 CrossRefGoogle Scholar
  32. 32.
    Nejdl W, Wolpers M, Siberski W, Schmitz C, Schlosser M, Brunkhorst I, L\ A, \#246, ser (2003) Super-peer-based routing and clustering strategies for RDF-based peer-to-peer networks. Paper presented at the Proceedings of the 12th international conference on World Wide Web, Budapest, HungaryGoogle Scholar
  33. 33.
    Pingley A, Yu W, Zhang N, Fu X, Zhao W (2009) CAP: a context-aware privacy protection system for location-based services. Paper presented at the Proceedings of the 2009 29th IEEE International Conference on Distributed Computing SystemsGoogle Scholar
  34. 34.
    Polat H, Du W (2003) Privacy-preserving collaborative filtering using randomized perturbation techniques. Paper presented at the Proceedings of the Third IEEE International Conference on Data MiningGoogle Scholar
  35. 35.
    Polat H, Du W (2005) SVD-based collaborative filtering with privacy. Paper presented at the Proceedings of the 2005 ACM symposium on Applied computing, Santa Fe, New MexicoGoogle Scholar
  36. 36.
    Pyshkin E, Kuznetsov A (2010) Approaches for web search user interfaces: how to improve the search quality for various types of information. Journal of Convergence 1:1–8Google Scholar
  37. 37.
    Reaz A, Raouf B (2010) A scalable peer-to-peer protocol enabling efficient and flexible searchGoogle Scholar
  38. 38.
    Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl-Based Syst 10 (5):557–570. doi: 10.1142/s0218488502001648 Google Scholar
  39. 39.
    Ye Y, Li X, Wu B, Li Y (2010) A comparative study of feature weighting methods for document co-clustering. Int J Inf Technol Commun Converg 1(2):206–220Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Telecommunications Software & Systems GroupWaterford Institute of TechnologyWaterfordIreland

Personalised recommendations