Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
A vector space model for automatic indexing
Communications of the ACM
A new privacy model for hiding group interests while accessing the Web
Proceedings of the 2002 ACM workshop on Privacy in the Electronic Society
Authorization and Privacy for Semantic Web Services
IEEE Intelligent Systems
PRAW—A PRivAcy model for the Web: Research Articles
Journal of the American Society for Information Science and Technology
Privacy practices of Internet users: self-reports versus observed behavior
International Journal of Human-Computer Studies - Special isssue: HCI research in privacy and security is critical now
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
A Comparative Study of Online Privacy Policies and Formats
PETS '09 Proceedings of the 9th International Symposium on Privacy Enhancing Technologies
Privacy preservation improvement by learning optimal profile generation rate
UM'03 Proceedings of the 9th international conference on User modeling
From t-Closeness-Like Privacy to Postrandomization via Information Theory
IEEE Transactions on Knowledge and Data Engineering
A privacy-protecting architecture for collaborative filtering via forgery and suppression of ratings
DPM'11 Proceedings of the 6th international conference, and 4th international conference on Data Privacy Management and Autonomous Spontaneus Security
Optimal tag suppression for privacy protection in the semantic Web
Data & Knowledge Engineering
Measuring the privacy of user profiles in personalized information systems
Future Generation Computer Systems
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We propose an architecture that preserves user privacy in the semantic Web via tag suppression. In tag suppression, users may wish to tag some resources and refrain from tagging some others in order to hinder privacy attackers in their efforts to profile users' interests. Following this strategy, our architecture helps users decide which tags should be suppressed. We describe the implementation details of the proposed architecture and provide further insight into the modeling of profiles. In addition, we present a mathematical formulation of the optimal tradeoff between privacy and tag suppression rate.