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Equitable differential privacy. [PDF]
Front 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 ...
Kaul V, Mukherjee T.
europepmc +4 more sources
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
doaj +4 more sources
Differential privacy in collaborative filtering recommender systems: a review. [PDF]
Front Big Data, 2023 State-of-the-art recommender systems produce high-quality recommendations to support users in finding relevant content. However, through the utilization of users' data for generating recommendations, recommender systems threaten users' privacy.
Müllner P, Lex E, Schedl M, Kowald D.
europepmc +2 more sources
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
doaj +4 more sources
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
openaire +5 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
doaj +3 more sources
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
doaj +1 more source
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
doaj +3 more sources
Distribution-invariant differential privacy
Journal of Econometrics, 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, Xuan, Shen, Xiaotong
openaire +4 more sources
"I need a better description": An Investigation Into User Expectations For Differential Privacy
The Journal of Privacy and Confidentiality, 2023 Despite recent widespread deployment of differential privacy, relatively little is known about what users think of differential privacy. In this work, we seek to explore users' privacy expectations related to differential privacy.
Rachel Cummings +2 more
doaj +3 more sources