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l-Diversity: Privacy Beyond k-Anonymity
Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called \kappa-anonymity has gainedExpand
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L-diversity: privacy beyond k-anonymity
Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called \kappa-anonymity has gainedExpand
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No free lunch in data privacy
Differential privacy is a powerful tool for providing privacy-preserving noisy query answers over statistical databases. It guarantees that the distribution of noisy query answers changes very littleExpand
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Privacy: Theory meets Practice on the Map
In this paper, we propose the first formal privacy analysis of a data anonymization process known as the synthetic data generation, a technique becoming popular in the statistics community. TheExpand
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Finding connected components in map-reduce in logarithmic rounds
Given a large graph G = (V, E) with millions of nodes and edges, how do we compute its connected components efficiently? Recent work addresses this problem in map-reduce, where a fundamentalExpand
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A rigorous and customizable framework for privacy
In this paper we introduce a new and general privacy framework called Pufferfish. The Pufferfish framework can be used to create new privacy definitions that are customized to the needs of a givenExpand
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Pufferfish: A framework for mathematical privacy definitions
In this article, we introduce a new and general privacy framework called Pufferfish. The Pufferfish framework can be used to create new privacy definitions that are customized to the needs of a givenExpand
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Blowfish privacy: tuning privacy-utility trade-offs using policies
Privacy definitions provide ways for trading-off the privacy of individuals in a statistical database for the utility of downstream analysis of the data. In this paper, we present Blowfish, a classExpand
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Worst-Case Background Knowledge for Privacy-Preserving Data Publishing
Recent work has shown the necessity of considering an attacker's background knowledge when reasoning about privacy in data publishing. However, in practice, the data publisher does not know whatExpand
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Principled Evaluation of Differentially Private Algorithms using DPBench
Differential privacy has become the dominant standard in the research community for strong privacy protection. There has been a flood of research into query answering algorithms that meet thisExpand
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