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Recommendation Systems: A Probabilistic Analysis
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Competitive recommendation systems
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Quantifying Privacy Leakage through Answering Database Queries
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Randomization in privacy preserving data mining
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XPref: a preference language for P3P
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Vision paper: enabling privacy for the paranoids
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XPref: a preference language for P3P
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Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents
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Application of Ɛ-testers algorithms under sketch and streaming calculation model in robot navigation
WSEAS Transactions on Computers
The sisterhood of the traveling packets
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A utility-theoretic approach to privacy in online services
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When do firms invest in privacy-preserving technologies?
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Robust methods for databases and robotics
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Anonymity meets game theory: secure data integration with malicious participants
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Towards a mechanism for incentivating privacy
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On honesty in sovereign information sharing
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Using personal portfolios to manage customer data
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Privacy consensus in anonymization systems via game theory
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Privacy and coordination: computing on databases with endogenous participation
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As individuals increasingly take advantage of on-line services, the value of the private information they possess emerges as a problem of fundamental concern. We believe that the principle underlying privacy should be simple: Individuals are entitled to control the dissemination of private information, disclosing it as part of a transaction only when they are fairly compensated. We show how this principle can be made precise in several diverse settings --- in the use of marketing surveys by a vendor designing a product; and in the design of collaborative filtering and recommendation systems, where we seek to quantify the value of each user's participation. Our approach draws on the analysis of coalitional games, making use of the core and Shapley value of such games as fair allocation principles.