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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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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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WaveCluster with Differential Privacy [PDF]
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015 WaveCluster is an important family of grid-based clustering algorithms that are capable of finding clusters of arbitrary shapes. In this paper, we investigate techniques to perform WaveCluster while ensuring differential privacy. Our goal is to develop a general technique for achieving differential privacy on WaveCluster that accommodates different ...
Ling Chen, Ting Yu 0001, Rada Chirkova
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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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