Differential privacy

In cryptography, differential privacy aims to provide means to maximize the accuracy of queries from statistical databases while minimizing the… (More)
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Papers overview

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Review
2017
Review
2017
Differential Privacy is a theoretical framework for ensuring the privacy of individual-level data when performing statistical… (More)
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Highly Cited
2016
Highly Cited
2016
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the… (More)
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Highly Cited
2013
Highly Cited
2013
The growing popularity of location-based systems, allowing unknown/untrusted servers to easily collect huge amounts of… (More)
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Highly Cited
2010
Highly Cited
2010
Privacy-preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions… (More)
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Highly Cited
2010
Highly Cited
2010
Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved {\em… (More)
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Highly Cited
2010
Highly Cited
2010
Differential privacy is a recent notion of privacy tailored to privacy-preserving data analysis [11]. Up to this point, research… (More)
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Highly Cited
2010
Highly Cited
2010
Privacy Integrated Queries (PINQ) is an extensible data analysis platform designed to provide unconditional privacy guarantees… (More)
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Highly Cited
2009
Highly Cited
2009
The definition of differential privacy has recently emerged as a leading standard of privacy guarantees for algorithms on… (More)
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Highly Cited
2007
Highly Cited
2007
We study the role that privacy-preserving algorithms, which prevent the leakage of specific information about participants, can… (More)
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Highly Cited
2006
Highly Cited
2006
In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the… (More)
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