Abstract
Recommender services that are currently used by IPTV providers help customers to find suitable content according to their preferences and increase overall content sales. Such systems provide competitive advantage over other IPTV providers and improve the overall performance of the current systems by building up an overlay that increases content availability, prioritization and distribution that is based on users’ interests. Current implementations are mostly centralized recommender service (CRS) where the information about the users’ profiles is stored in a single server. This type of design poses a severe privacy hazard, since the users’ profiles are fully under the control of the CRS and the users have to fully trust the CRS to keep their profiles private. In this paper, we present our approach to build a private centralized recommender service (PCRS) using collaborative filtering techniques and an agent based middleware for private recommendations (AMPR). The AMPR ensures user profile privacy in the recommendation process. We introduce two obfuscation algorithms embedded in the AMPR that protect users’ profile privacy as well as preserve the aggregates in the dataset in order to maximize the usability of information for accurate recommendations. Using these algorithms provides the user complete control on the privacy of his personal profile. We also provide an IPTV network scenario that uses AMPR and its evaluations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hand, S., Varan, D.: Interactive Narratives: Exploring the Links between Empathy, Interactivity and Structure, pp. 11–19 (2008)
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)
Dialogue, C.: Cyber Dialogue Survey Data Reveals Lost Revenue for Retailers Due to Widespread Consumer Privacy Concerns. Cyber Dialogue (2001)
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)
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)
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)
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)
Canny, J.: Collaborative Filtering with Privacy. In: Proceedings of the 2002 IEEE Symposium on Security and Privacy, p. 45. IEEE Computer Society (2002)
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)
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)
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)
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)
Parameswaran, R., Blough, D.M.: Privacy preserving data obfuscation for inherently clustered data. Int. J. Inf. Comput. Secur. 2, 4–26 (2008)
Elmisery, A., Botvich, D.: Private Recommendation Service For IPTV System. In: 12th IFIP/IEEE International Symposium on Integrated Network Management. IEEE, Dublin (2011)
Blaze, M., Schneier, B.: The MacGuffin block cipher algorithm, pp. 97–110 (1995)
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)
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)
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)
Adams, A., Gelfand, N., Dolson, J., Levoy, M.: Gaussian KD-trees for fast high-dimensional filtering. ACM Trans. Graph. 28, 1–12 (2009)
Lam, S., Herlocker, J.: MovieLens Data Sets. Department of Computer Science and Engineering at the University of Minnesota (2006)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Elmisery, A.M., Botvich, D. (2012). Agent Based Middleware for Maintaining User Privacy in IPTV Recommender Services. In: Prasad, R., Farkas, K., Schmidt, A.U., Lioy, A., Russello, G., Luccio, F.L. (eds) Security and Privacy in Mobile Information and Communication Systems. MobiSec 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30244-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-30244-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30243-5
Online ISBN: 978-3-642-30244-2
eBook Packages: Computer ScienceComputer Science (R0)