Results 11 to 20 of about 1,916,192 (291)
Heterogeneous Differential Privacy
The Journal of Privacy and Confidentiality, 2017 The massive collection of personal data by personalization systems has rendered the preservation of privacy of individuals more and more difficult. Most of the proposed approaches to preserve privacy in personalization systems usually address this issue ...
Mohammad Alaggan +2 more
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The Journal of Privacy and Confidentiality, 2013 We propose a relaxed privacy definition called {\em random differential privacy} (RDP). Differential privacy requires that adding any new observation to a database will have small effect on the output of the data-release procedure.
Robert Hall +2 more
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On the 'Semantics' of Differential Privacy: A Bayesian Formulation [PDF]
The Journal of Privacy and Confidentiality, 2014 Differential privacy is a definition of privacy for algorithms that analyze and publish information about statistical databases. It is often claimed that differential privacy provides guarantees against adversaries with arbitrary side information.
Shiva P. Kasiviswanathan, Adam Smith
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Privacy-Preserving Monotonicity of Differential Privacy Mechanisms
Applied Sciences, 2018 Differential privacy mechanisms can offer a trade-off between privacy and utility by using privacy metrics and utility metrics. The trade-off of differential privacy shows that one thing increases and another decreases in terms of privacy metrics and ...
Hai Liu +5 more
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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.
Damien Desfontaines, Balázs Pejó
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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.
Shuheng Zhou 0002 +2 more
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Asymmetric Differential Privacy
CoRR, 2021 Differential privacy (DP) is getting attention as a privacy definition when publishing statistics of a dataset. This paper focuses on the limitation that DP inevitably causes two-sided error, which is not desirable for epidemic analysis such as how many COVID-19 infected individuals visited location A.
Shun Takagi +2 more
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On the geometry of differential privacy [PDF]
Proceedings of the forty-second ACM symposium on Theory of computing, 2010 We consider the noise complexity of differentially private mechanisms in the setting where the user asks $d$ linear queries $f\colon\Rn\to\Re$ non-adaptively. Here, the database is represented by a vector in $\Rn$ and proximity between databases is measured in the $\ell_1$-metric.
Moritz Hardt, Kunal Talwar
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CoRR, 2021 Differential privacy (DP) is a widely-accepted and widely-applied notion of privacy based on worst-case analysis. Often, DP classifies most mechanisms without additive noise as non-private (Dwork et al., 2014). Thus, additive noises are added to improve privacy (to achieve DP).
Ao Liu 0001 +2 more
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Verifiable differential privacy [PDF]
Proceedings of the Tenth European Conference on Computer Systems, 2015 Working with sensitive data is often a balancing act between privacy and integrity concerns. Consider, for instance, a medical researcher who has analyzed a patient database to judge the effectiveness of a new treatment and would now like to publish her findings.
Arjun Narayan +3 more
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