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Equitable differential privacy [PDF]
Frontiers in Big DataDifferential privacy (DP) has been in the public spotlight since the announcement of its use in the 2020 U.S. Census. While DP algorithms have substantially improved the confidentiality protections provided to Census respondents, concerns have been ...
Vasundhara Kaul, Tamalika Mukherjee
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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 ...
Weijie Su
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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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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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Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees [PDF]
IEEE Transactions on Information Forensics and Security, 2017 Differential privacy is a popular privacy model within the research community because of the strong privacy guarantee it offers, namely that the presence or absence of any individual in a data set does not significantly influence the results of analyses on the data set.
Josep Domingo-Ferrer +2 more
exaly +6 more sources
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
The Journal of Privacy and Confidentiality, 2021 We give a simple, computationally efficient, and node-differentially-private algorithm for estimating the parameter of an Erdos-Renyi graph---that is, estimating p in a G(n,p)---with near-optimal accuracy.
Adam Sealfon, Jonathan Ullman
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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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Review of Differential Privacy Research [PDF]
Jisuanji kexue, 2023 In the past decade,widespread data collection has become the norm.With the rapid development of large-scale data analysis and machine learning,data privacy is facing fundamental challenges.Exploring the trade-offs between privacy protection and data ...
ZHAO Yuqi, YANG Min
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Privacy view and target of differential privacy
网络与信息安全学报, 2023 The study aimed to address the challenges in understanding the privacy goals of differential privacy by analyzing the privacy controversies surrounding it in various fields.It began with the example of data correlation and highlighted the differing ...
Jingyu JIA, Chang TAN, Zhewei LIU, Xinhao LI, Zheli LIU, Tao ZHANG
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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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