Results 21 to 30 of about 64,907 (298)
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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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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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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"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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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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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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Learning With Differential Privacy [PDF]
, 2020 The leakage of data might have an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection systems are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized ...
Poushali Sengupta +2 more
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