Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Practical multi-candidate election system
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Collaborative filtering with privacy via factor analysis
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Non-Interactive and Information-Theoretic Secure Verifiable Secret Sharing
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Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Shilling recommender systems for fun and profit
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PocketLens: Toward a personal recommender system
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TrustGuard: countering vulnerabilities in reputation management for decentralized overlay networks
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SVD-based collaborative filtering with privacy
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Classification features for attack detection in collaborative recommender systems
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Secure two-party k-means clustering
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Online subscriptions with anonymous access
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Privacy-Preserving Recommendation Systems for Consumer Healthcare Services
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Sub-linear zero-knowledge argument for correctness of a shuffle
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Differential privacy: a survey of results
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Privacy-preserving collaborative recommender systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Verifiable Secret Shuffle of Homomorphic Encryptions
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APCCM '10 Proceedings of the Seventh Asia-Pacific Conference on Conceptual Modelling - Volume 110
Reliable medical recommendation systems with patient privacy
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A Private and Reliable Recommendation System for Social Networks
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One of the concerns patients have when confronted with a medical condition is which physician to trust. Any recommendation system that seeks to answer this question must ensure that any sensitive medical information collected by the system is properly secured. In this article, we codify these privacy concerns in a privacy-friendly framework and present two architectures that realize it: the Secure Processing Architecture (SPA) and the Anonymous Contributions Architecture (ACA). In SPA, patients submit their ratings in a protected form without revealing any information about their data and the computation of recommendations proceeds over the protected data using secure multiparty computation techniques. In ACA, patients submit their ratings in the clear, but no link between a submission and patient data can be made. We discuss various aspects of both architectures, including techniques for ensuring reliability of computed recommendations and system performance, and provide their comparison.