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t-Closeness was introduced as an improvement of the well-known k-anonymity privacy model for data release. On the other hand, e-differential privacy was originally proposed as a privacy property for answers to on-line database queries and it has been very welcome in academic circles. In spite of their quite diverse origins and motivations, we show in this paper that t-closeness and e-differential...
Performing some task among a set of agents requires the use of some protocol that regulates the interactions between them. If those agents are rational, they may try to subvert the protocol for their own benefit, in an attempt to reach an outcome that provides greater utility. We revisit the traditional notion of self-enforcing protocols implemented using existing game-theoretic solution concepts,...
We introduce a novel mechanism to attain differential privacy. Contrary to the common mechanism based on the addition of a noise whose magnitude is proportional to the sensitivity of the query function, our proposal is based on the refinement of the user's prior knowledge about the response. We show that our mechanism has several advantages over noise addition: it does not require complex computations,...
t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the entire data set is no more than a threshold t. Here, we define a privacy measure in terms of...
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