GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Super-peer-based routing and clustering strategies for RDF-based peer-to-peer networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
SVD-based collaborative filtering with privacy
Proceedings of the 2005 ACM symposium on Applied computing
PRIVE: anonymous location-based queries in distributed mobile systems
Proceedings of the 16th international conference on World Wide Web
Tor: the second-generation onion router
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Experimental Demonstration of a Hybrid Privacy-Preserving Recommender System
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
CAP: A Context-Aware Privacy Protection System for Location-Based Services
ICDCS '09 Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems
Graph-Based Data Clustering with Overlaps
COCOON '09 Proceedings of the 15th Annual International Conference on Computing and Combinatorics
Privacy Preserving Distributed Learning Clustering of HealthCare Data Using Cryptography Protocols
COMPSACW '10 Proceedings of the 2010 IEEE 34th Annual Computer Software and Applications Conference Workshops
Privacy Aware Recommender Service for IPTV Networks
MUE '11 Proceedings of the 2011 Fifth FTRA International Conference on Multimedia and Ubiquitous Engineering
An attacker's view of distance preserving maps for privacy preserving data mining
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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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