Multimedia Tools and Applications

, Volume 75, Issue 22, pp 14927–14957 | Cite as

Collaborative privacy framework for minimizing privacy risks in an IPTV social recommender service

  • Ahmed M. Elmisery
  • Seungmin RhoEmail author
  • Dmitri Botvich


In our connected world, recommender systems have become widely known for their ability to provide expert and personalized referrals to end-users in different domains. The rapid growth of social networks has given a rise to a new kind of systems, which have been termed “social recommender service”. In this context, a software as a service recommender system can be utilized to extract a set of suitable referrals for certain users based on the data collected from the personal profiles of other end-users within a social structure. However, preserving end-users privacy in social recommender services is a very challenging problem that might prevent privacy concerned users from releasing their own profiles’ data or to be forced to release an erroneous data. Thus, both cases can detain the accuracy of extracted referrals. So in order to gain accurate referrals, the social recommender service should have the ability to preserve the privacy of end-users registered in their system. In this paper, we present a middleware that runs on the end-users’ side in order to conceal their profiles data when being released for the recommendation purposes. The computation of recommendation proceeds over this concealed data. The proposed middleware is equipped with a distributed data collection protocol along with two stage concealment process to give the end-users complete control over the privacy of their profiles. We will present an IPTV network scenario along with the proposed middleware. A number of different experiments were performed on real data which was concealed using our two stage concealment process to evaluate the achieved privacy and accuracy of the extracted referrals. As supported by the experiments, the proposed framework maintains the recommendations accuracy with a reasonable privacy level.


Privacy Clustering IPTV network Recommendation systems 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2013R1A1A2061978)


  1. 1.
    Ardissono L, Kobsa A, Maybury M (2004) Personalized digital television: targeting programs to individual viewers, vol 6, Human-Computer Interaction Series. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar
  2. 2.
    Beimel A, Nissim K, Omri E (2011) Distributed private data analysis: on simultaneously solving How and What. arXiv preprint arXiv:1103.2626Google Scholar
  3. 3.
    Blum A, Dwork C, McSherry F, Nissim K (2005) Practical privacy: the SuLQ framework. Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems. ACM, 128–138Google Scholar
  4. 4.
    Canny J (2002) Collaborative filtering with privacy via factor analysis. Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval. ACM, Tampere, pp 238–245Google Scholar
  5. 5.
    Canny J (2002) Collaborative filtering with privacy. Proceedings of the 2002 I.E. symposium on security and privacy. IEEE Computer Society 45Google Scholar
  6. 6.
    Carbo J, Molina J, Davila J (2002) Trust management through fuzzy reputation. Int J Coop Inf Syst 12:135–155CrossRefGoogle Scholar
  7. 7.
    Commission E (2002) Directive 2002/58/EC of the European Parliament and of the Council of 12 July 2002 concerning the processing of personal data and the protection of privacy in the electronic communications sector. Official Journal L 201: 07Google Scholar
  8. 8.
    Cranor LF (2003) ‘I didn’t buy it for myself’ privacy and ecommerce personalization. Proceedings of the 2003 ACM workshop on privacy in the electronic society. ACM, WashingtonGoogle Scholar
  9. 9.
    Directive E (1995) 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Official Journal of the EC 23: 6Google Scholar
  10. 10.
    Domingo-Ferrer J (2009) Record linkage. In: Liu L, Özsu MT (eds) Encyclopedia of database systems. Springer, US, pp 2353–2354Google Scholar
  11. 11.
    Dwork C (2006) Differential privacy. Automata, languages and programming. Springer, New York, pp 1–12CrossRefzbMATHGoogle Scholar
  12. 12.
    Eichhorn BH, Hayre LS (1983) Scrambled randomized response methods for obtaining sensitive quantitative data. J Stat Plan Infer 7:307–316CrossRefGoogle Scholar
  13. 13.
    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, vol 10. Springer, Berlin, pp 821–832CrossRefGoogle Scholar
  14. 14.
    Elmisery A, Botvich D (2011) Private recommendation service for IPTV system. 12th IFIP/IEEE international symposium on integrated network management. IEEE, DublinGoogle Scholar
  15. 15.
    Elmisery A, Botvich D (2011) Agent based middleware for maintaining user privacy in IPTV recommender services. 3rd international ICST conference on security and privacy in mobile information and communication systems. ICST, AalborgGoogle Scholar
  16. 16.
    Elmisery A, Botvich D (2011) Privacy aware obfuscation middleware for mobile jukebox recommender services. The 11th IFIP conference on e-Business, e-Service, e-Society. IFIP, KaunasGoogle Scholar
  17. 17.
    Elmisery A, Botvich D (2011) Privacy aware recommender service for IPTV networks. 5th FTRA/IEEE international conference on multimedia and ubiquitous engineering. IEEE, CreteGoogle Scholar
  18. 18.
    Elmisery A, Botvich D (2011) Agent based middleware for private data mashup in IPTV recommender services. 16th IEEE international workshop on computer aided modeling, analysis and design of communication links and networks. IEEE, KyotoGoogle Scholar
  19. 19.
    Elmisery AM, Doolin K, Botvich D (2012) Privacy aware community based recommender service for conferences attendees. 16th international conference on knowledge-based and intelligent information & engineering systems, vol 243. Ios Press, San Sebastian, pp 519–531Google Scholar
  20. 20.
    Esma A (2008) Experimental demonstration of a hybrid privacy-preserving recommender system. In: Gilles B, Jose MF, Flavien Serge Mani O, Zbigniew R (eds.) Vol. 0 161–170Google Scholar
  21. 21.
    Gemmis MD, Iaquinta L, Lops P, Musto C, Narducci F, Semeraro G (2009) Preference learning in recommender systems. European conference on machine learning and principles and practice of knowledge discovery in databases (ECML/PKDD). ACM, SloveniaGoogle Scholar
  22. 22.
    Golbeck J, Hendler J (2006) FilmTrust: movie recommendations using trust in web-based social networks. Consumer communications and networking conference, 2006. CCNC 2006. 3rd IEEE, Vol. 1 282–286Google Scholar
  23. 23.
    Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22:5–53CrossRefGoogle Scholar
  24. 24.
    Hong T, Tsamis D (2006) Use of knn for the netflix prize.
  25. 25.
    Huang Z, Chen H, Zeng D (2004) Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans Inf Syst 22:116–142CrossRefGoogle Scholar
  26. 26.
    Huang Z, Du W, Chen B (2005) Deriving private information from randomized data. Proceedings of the 2005 ACM SIGMOD international conference on management of data. ACM, Baltimore, pp 37–48Google Scholar
  27. 27.
    Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. Proceedings of the thirtieth annual ACM symposium on theory of computing. ACM, Dallas, pp 604–613zbMATHGoogle Scholar
  28. 28.
    Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  29. 29.
    Jeckmans AJ, Beye M, Erkin Z, Hartel P, Lagendijk RL, Tang Q (2013) Privacy in recommender systems. Social media retrieval. Springer, New York, pp 263–281CrossRefGoogle Scholar
  30. 30.
    Kargupta H, Datta S, Wang Q, Sivakumar K (2003) On the privacy preserving properties of random data perturbation techniques. Proceedings of the third IEEE international conference on data mining. IEEE Computer Society 99Google Scholar
  31. 31.
    Kawazoe K, Kakinuma R, Haneda Y, Minoura D, Minamoto S, Ishimoto H (2007) Platform application technology using the next generation network. Technical Review. NTTGoogle Scholar
  32. 32.
    Kelly D, Teevan J (2003) Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37:18–28CrossRefGoogle Scholar
  33. 33.
    Konstan JA (2004) Introduction to recommender systems: algorithms and evaluation. ACM Trans Inf Syst (TOIS) 22:1–4CrossRefGoogle Scholar
  34. 34.
    Lam S, Herlocker J (2006) MovieLens data sets. Department of Computer Science and Engineering at the University of MinnesotaGoogle Scholar
  35. 35.
    Lewis DD (1998) Naive (Bayes) at forty: the independence assumption in information retrieval. proceedings of the 10th European conference on machine learning. Springer, New York, pp 4–15Google Scholar
  36. 36.
    Lin J-L, Cheng Y-W (2009) Privacy preserving itemset mining through noisy items. Expert Syst Appl 36:5711–5717CrossRefGoogle Scholar
  37. 37.
    Lin J-L, Liu JY-C (2007) Privacy preserving itemset mining through fake transactions. Proceedings of the 2007 ACM symposium on applied computing. ACM, Seoul, pp 375–379Google Scholar
  38. 38.
    Margulis ST (2003) On the status and contribution of westin’s and altman’s theories of privacy. J Soc Issues 59:411–429CrossRefGoogle Scholar
  39. 39.
    McSherry F, Mironov I (2009) Differentially private recommender systems: building privacy into the net. Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Paris, pp 627–636Google Scholar
  40. 40.
    Miller BN, Konstan JA, Riedl J (2004) PocketLens: toward a personal recommender system. ACM Trans Inf Syst 22:437–476CrossRefGoogle Scholar
  41. 41.
    Narayanan A, Shmatikov V (2008) Robust De-anonymization of large sparse datasets. Proceedings of the 2008 I.E. symposium on security and privacy. IEEE Computer SocietyGoogle Scholar
  42. 42.
    Nejdl W, Wolpers M, Siberski W, Schmitz C, Schlosser M, Brunkhorst I (2003) Super-peer-based routing and clustering strategies for RDF-based peer-to-peer networks. Proceedings of the 12th international conference on World Wide Web. ACM, Budapest, pp 536–543Google Scholar
  43. 43.
    Nissim K, Raskhodnikova S, Smith A (2007) Smooth sensitivity and sampling in private data analysis. Proceedings of the thirty-ninth annual ACM symposium on theory of computing. ACM, 75–84Google Scholar
  44. 44.
    Parameswaran R, Blough DM (2008) Privacy preserving data obfuscation for inherently clustered data. Int J Inf Comput Secur 2:4–26Google Scholar
  45. 45.
    Polat H, Du W (2003) Privacy-preserving collaborative filtering using randomized perturbation techniques. Proceedings of the third IEEE international conference on data mining. IEEE Computer Society625Google Scholar
  46. 46.
    Polat H, Du W (2005) SVD-based collaborative filtering with privacy. Proceedings of the 2005 ACM symposium on applied computing. ACM, Santa Fe, pp 791–795CrossRefGoogle Scholar
  47. 47.
    Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. Recommender systems handbook. Springer, New York, pp 1–35zbMATHGoogle Scholar
  48. 48.
    Shokri R, Pedarsani P, Theodorakopoulos G, Hubaux J-P (2009) Preserving privacy in collaborative filtering through distributed aggregation of offline profiles. Proceedings of the third ACM conference on recommender systems. ACM, 157–164Google Scholar
  49. 49.
    Thuraisingham B (2002) Data mining, national security, privacy and civil liberties. SIGKDD. Explor Newsl 4:1–5CrossRefGoogle Scholar
  50. 50.
    Ziegler C-N, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. Proceedings of the 14th international conference on World Wide Web. ACM, Chiba, pp 22–32Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ahmed M. Elmisery
    • 1
  • Seungmin Rho
    • 2
    Email author
  • Dmitri Botvich
    • 1
  1. 1.TSSG, Waterford Institute of Technology-WIT-CoWaterfordIreland
  2. 2.Department of MultimediaSungkyul UniversityAnyang-siSouth Korea

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