Results 21 to 30 of about 58,983 (255)
2022 IEEE International Symposium on Information Theory (ISIT), 2022 To appear in the 2022 IEEE International Symposium on Information ...
Ziqi Zhou 0005 +5 more
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LinkedIn's Audience Engagements API
The Journal of Privacy and Confidentiality, 2021 We present a privacy system that leverages differential privacy to protect LinkedIn members' data while also providing audience engagement insights to enable marketing analytics related applications.
Ryan Rogers +7 more
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Trans. Mach. Learn. Res., 2021 Differential privacy (DP) is a widely-accepted and widely-applied notion of privacy based on worst-case analysis. Often, DP classifies most mechanisms without additive noise as non-private (Dwork et al., 2014). Thus, additive noises are added to improve privacy (to achieve DP).
Ao Liu 0001 +2 more
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Rényi Differential Privacy [PDF]
2017 IEEE 30th Computer Security Foundations Symposium (CSF), 2017 We propose a natural relaxation of differential privacy based on the Renyi divergence. Closely related notions have appeared in several recent papers that analyzed composition of differentially private mechanisms. We argue that the useful analytical tool can be used as a privacy definition, compactly and accurately representing guarantees on the tails ...
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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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Asymmetric Differential Privacy
CoRR, 2021 Differential privacy (DP) is getting attention as a privacy definition when publishing statistics of a dataset. This paper focuses on the limitation that DP inevitably causes two-sided error, which is not desirable for epidemic analysis such as how many COVID-19 infected individuals visited location A.
Shun Takagi +2 more
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Privacy-Preserving Bin-Packing With Differential Privacy
IEEE Open Journal of Signal Processing, 2022 With the emerging of e-commerce, package theft is at a high level: It is reported that 1.7 million packages are stolen or lost every day in the U.S. in 2020, which costs $25 million every day for the lost packages and the service.
Tianyu Li +2 more
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Liftings for Differential Privacy
CoRR, 2017 Recent developments in formal verification have identified approximate liftings (also known as approximate couplings) as a clean, compositional abstraction for proving differential privacy. There are two styles of definitions for this construction. Earlier definitions require the existence of one or more witness distributions, while a recent definition
Gilles Barthe +4 more
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Differential Privacy By Sampling
CoRR, 2017 In this paper we present the Sampling Privacy mechanism for privately releasing personal data. Sampling Privacy is a sampling based privacy mechanism that satisfies differential privacy.
Josh Joy, Mario Gerla
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Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need to respect such constraints on the sanitized data product as a primary utility requirement. Invariants challenge the formulation, implementation, and interpretation of privacy guarantees.
Jie Gao 0001, Ruobin Gong, Fang-Yi Yu
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