Results 21 to 30 of about 212,227 (315)
SoK: Differential privacies [PDF]
Proceedings on Privacy Enhancing Technologies, 2020 AbstractShortly after it was first introduced in 2006,differential privacybecame the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions.
Desfontaines, Damien, Pejó, Balázs
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Lower bounds in differential privacy [PDF]
, 2011 Corrected some minor errors and typos.
Anindya De
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Gaussian Differential Privacy [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2022 AbstractIn the past decade, differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy. This privacy definition and its divergence based relaxations, however, have several acknowledged weaknesses, either in handling composition of private algorithms or in analysing important primitives like privacy ...
Dong, Jinshuo +2 more
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Distribution-Invariant Differential Privacy. [PDF]
J Econom, 2023 Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public health, information technology, and the U.S. decennial census. Nevertheless, to guarantee differential privacy, existing methods may unavoidably alter the conclusion of the original data ...
Bi X, Shen X.
europepmc +4 more sources
On the Differential Privacy of Bayesian Inference
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on probabilistic graphical models.
Zuhe Zhang +2 more
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A logical characterization of differential privacy [PDF]
Science of Computer Programming, 2020 Differential privacy is a formal definition of privacy ensuring that sensitive information relative to individuals cannot be inferred by querying a database. In this paper, we exploit a modeling of this framework via labeled Markov Chains (LMCs) to provide a logical characterization of differential privacy: we consider a probabilistic variant of the ...
Castiglioni, Valentina +2 more
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Differential privacy with compression [PDF]
2009 IEEE International Symposium on Information Theory, 2009 This work studies formal utility and privacy guarantees for a simple multiplicative database transformation, where the data are compressed by a random linear or affine transformation, reducing the number of data records substantially, while preserving the number of original input variables.
Katrina Ligett +2 more
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Liftings for Differential Privacy
arXiv: Logic in Computer Science, 2017 Recent developments in formal verification have identified approximate liftings (also known as approximate couplings) as a clean, compositional abstraction for proving differential privacy. There are two styles of definitions for this construction. Earlier definitions require the existence of one or more witness distributions, while a recent definition
Barthe, Gilles +4 more
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Aggressive Behavior, Volume 49, Issue 1, Page 5-14, January 2023., 2023 Abstract This study uses longitudinal data from the UK Millennium Cohort Study (N = 13,277) to examine the childhood and early adolescence factors that predict weapon involvement in middle adolescence, which in this study is exemplified by having carried or used a weapon.
Aase Villadsen, Emla Fitzsimons
wiley +1 more source
WaveCluster with Differential Privacy [PDF]
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015 WaveCluster is an important family of grid-based clustering algorithms that are capable of finding clusters of arbitrary shapes. In this paper, we investigate techniques to perform WaveCluster while ensuring differential privacy. Our goal is to develop a general technique for achieving differential privacy on WaveCluster that accommodates different ...
Ting Yu, Ling Chen, Rada Chirkova
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