GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
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
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
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Private distributed collaborative filtering using estimated concordance measures
Proceedings of the 2007 ACM conference on Recommender systems
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
An Architecture for Privacy Preserving Collaborative Filtering on Web Portals
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Improved recommendations via (more) collaboration
Procceedings of the 13th International Workshop on the Web and Databases
A framework for privacy-conducive recommendations
Proceedings of the 9th annual ACM workshop on Privacy in the electronic society
A game theoretic framework for data privacy preservation in recommender systems
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Shared collaborative filtering
Proceedings of the fifth ACM conference on Recommender systems
Pistis: A Privacy-Preserving Content Recommender System for Online Social Communities
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
The tradeoffs of societal computing
Proceedings of the 10th SIGPLAN symposium on New ideas, new paradigms, and reflections on programming and software
The decreasing marginal value of evaluation network size
ACM SIGCAS Computers and Society
The impact of data obfuscation on the accuracy of collaborative filtering
Expert Systems with Applications: An International Journal
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Privacy-preserving content-based recommender system
Proceedings of the on Multimedia and security
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In recommender systems, usually, a central server needs to have access to users' profiles in order to generate useful recommendations. Having this access, however, undermines the users' privacy. The more information is revealed to the server on the user-item relations, the lower the users' privacy is. Yet, hiding part of the profiles to increase the privacy comes at the cost of recommendation accuracy or difficulty of implementing the method. In this paper, we propose a distributed mechanism for users to augment their profiles in a way that obfuscates the user-item connection to an untrusted server, with minimum loss on the accuracy of the recommender system. We rely on the central server to generate the recommendations. However, each user stores his profile offline, modifies it by partly merging it with the profile of similar users through direct contact with them, and only then periodically uploads his profile to the server. We propose a metric to measure privacy at the system level, using graph matching concepts. Applying our method to the Netflix prize dataset, we show the effectiveness of the algorithm in solving the tradeoff between privacy and accuracy in recommender systems in an applicable way.