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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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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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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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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 Profiles and Amplification by Subsampling
The Journal of Privacy and Confidentiality, 2020 Differential privacy provides a robust quantifiable methodology to measure and control the privacy leakage of data analysis algorithms. A fundamental insight is that by forcing algorithms to be randomized, their privacy leakage can be characterized by ...
Borja Balle +2 more
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"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
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State-Based Differential Privacy Verification and Enforcement for Probabilistic Automata
Mathematics, 2023 Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete
Yuanxiu Teng, Zhiwu Li, Li Yin, Naiqi Wu
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Survey on Privacy Protection Solutions for Recommended Applications [PDF]
Jisuanji kexue, 2021 In the context of the era of big data,various industries want to train recommendation models based on user behavior data to provide users with accurate recommendations.The common characteristics of the used data are huge amount,carrying sensitive ...
DONG Xiao-mei, WANG Rui, ZOU Xin-kai
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Investigation and Application of Differential Privacy in Bitcoin
IEEE Access, 2022 Bitcoin is one of the best-known cryptocurrencies, which captivated researchers with its innovative blockchain structure. Examinations of this public blockchain resulted in many proposals for improvement in terms of anonymity and privacy.
Merve Can Kus, Albert Levi
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Per-instance Differential Privacy
The Journal of Privacy and Confidentiality, 2019 We consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set.
Yu-Xiang Wang
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