Results 21 to 30 of about 1,916,192 (291)
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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A multimodal differential privacy framework based on fusion representation learning [PDF]
, 2022 Differential privacy mechanisms vary in modalities, and there have been many methods implementing differential privacy on unimodal data. Few studies focus on unifying them to protect multimodal data, though privacy protection of multimodal data is of ...
Chaoxin Cai +5 more
core +1 more source
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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Randomized Privacy Budget Differential Privacy
CoRR, 2022 arXiv admin note: text overlap with arXiv:2009 ...
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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
doaj +1 more source
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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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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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
doaj +1 more source
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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