Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Building consumer trust online
Communications of the ACM
A vector space model for automatic indexing
Communications of the ACM
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Convex Optimization
'I didn't buy it for myself' privacy and ecommerce personalization
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
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
IEEE Transactions on Knowledge and Data Engineering
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Specification of a framework for the anonymous use of privileges
Telematics and Informatics - Special issue: Developing a culture of privacy in the global village
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
The SPARTA pseudonym and authorization system
Science of Computer Programming
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
Privacy-preserving collaborative recommender systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Optimized query forgery for private information retrieval
IEEE Transactions on Information Theory
Coprivacy: towards a theory of sustainable privacy
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
A privacy-preserving architecture for the semantic web based on tag suppression
TrustBus'10 Proceedings of the 7th international conference on Trust, privacy and security in digital business
Measuring the privacy of user profiles in personalized information systems
Future Generation Computer Systems
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Recommendation systems are information-filtering systems that help users deal with information overload. Unfortunately, current recommendation systems prompt serious privacy concerns. In this work, we propose an architecture that protects user privacy in such collaborative-filtering systems, in which users are profiled on the basis of their ratings. Our approach capitalizes on the combination of two perturbative techniques, namely the forgery and the suppression of ratings. In our scenario, users rate those items they have an opinion on. However, in order to avoid privacy risks, they may want to refrain from rating some of those items, and/or rate some items that do not reflect their actual preferences. On the other hand, forgery and suppression may degrade the quality of the recommendation system. Motivated by this, we describe the implementation details of the proposed architecture and present a formulation of the optimal trade-off among privacy, forgery rate and suppression rate. Finally, we provide a numerical example that illustrates our formulation.