Agent based middleware for maintaining user privacy in IPTV recommender services

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering

Volumn 94 LNICST, Issue , 2012, Pages 64-75

  Elmisery, Ahmed M ^a   Botvich, Dmitri ^a  

^a Ireland   (Ireland)

Author keywords

Clustering; IPTV Networks; Multi Agent Systems; Privacy; Recommender System

Indexed keywords

AGENT BASED; AGENT SYSTEMS; CLUSTERING; COLLABORATIVE FILTERING TECHNIQUES; COMPETITIVE ADVANTAGE; COMPLETE CONTROL; CONTENT AVAILABILITY; DATA SETS; IPTV NETWORKS; ITS EVALUATION; PERSONAL PROFILE; PRIORITIZATION; SINGLE SERVER; USER PRIVACY; USER PROFILE; USERS' INTERESTS;

ALGORITHMS; COMMUNICATION SYSTEMS; COMPETITION; DATA PRIVACY; IPTV; MULTI AGENT SYSTEMS; RECOMMENDER SYSTEMS;

MIDDLEWARE;

EID: 84869597343     PISSN: 18678211     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-30244-2_6     Document Type: Conference Paper

References (22)

1. Hand, S., Varan, D.: Interactive Narratives: Exploring the Links between Empathy, Interactivity and Structure, pp. 11-19 (2008)
2. Cranor, L.F.: 'I didn't buy it for myself' privacy and ecommerce personalization. In: Proceedings of the 2003 ACM Workshop on Privacy in the Electronic Society. ACM, Washington, DC (2003)
3. Dialogue, C.: Cyber Dialogue Survey Data Reveals Lost Revenue for Retailers Due to Widespread Consumer Privacy Concerns. Cyber Dialogue (2001)
4. Narayanan, A., Shmatikov, V.: Robust De-anonymization of Large Sparse Datasets. In: Proceedings of the 2008 IEEE Symposium on Security and Privacy. IEEE Computer Society (2008)
5. McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the net. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 627-636. ACM, Paris (2009)
6. Esma, A.: Experimental Demonstration of a Hybrid Privacy-Preserving Recommender System. In: Gilles, B., Jose, M.F., Flavien Serge Mani, O., Zbigniew, R. (eds.), 161-170 (2008)
7. Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 238-245. ACM, Tampere (2002)
8. Canny, J.: Collaborative Filtering with Privacy. In: Proceedings of the 2002 IEEE Symposium on Security and Privacy, p. 45. IEEE Computer Society (2002)
9. Polat, H., Du, W.: Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques. In: Proceedings of the Third IEEE International Conference on Data Mining, p. 625. IEEE Computer Society (2003)
10. Polat, H., Du, W.: SVD-based collaborative filtering with privacy. In: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 791-795. ACM, Santa Fe (2005)
11. Huang, Z., Du, W., Chen, B.: Deriving private information from randomized data. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 37-48. ACM, Baltimore (2005)
12. Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the Privacy Preserving Properties of Random Data Perturbation Techniques. In: Proceedings of the Third IEEE International Conference on Data Mining, p. 99. IEEE Computer Society (2003)
13. Parameswaran, R., Blough, D.M.: Privacy preserving data obfuscation for inherently clustered data. Int. J. Inf. Comput. Secur. 2, 4-26 (2008)
14. Elmisery, A., Botvich, D.: Private Recommendation Service For IPTV System. In: 12th IFIP/IEEE International Symposium on Integrated Network Management. IEEE, Dublin (2011)
15. Blaze, M., Schneier, B.: The MacGuffin block cipher algorithm, pp. 97-110 (1995)
16. Elmisery, A.M., Huaiguo, F.: Privacy Preserving Distributed Learning Clustering Of HealthCare Data Using Cryptography Protocols. In: 34th IEEE Annual International Computer Software and Applications Workshops, Seoul, South Korea (2010)
17. Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21 (2003)
18. Xu, K., Li, Y., Ju, T., Hu, S.-M., Liu, T.-Q.: Efficient affinity-based edit propagation using K-D tree. In: ACM SIGGRAPH Asia 2009 Papers, pp. 1-6. ACM, Yokohama (2009)
19. Adams, A., Gelfand, N., Dolson, J., Levoy, M.: Gaussian KD-trees for fast highdimensional filtering. ACM Trans. Graph. 28, 1-12 (2009)
20. Lam, S., Herlocker, J.: MovieLens Data Sets. Department of Computer Science and Engineering at the University of Minnesota (2006)
21. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230-237. ACM, Berkeley (1999)
22. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5-53 (2004)