Results 221 to 230 of about 58,983 (255)

An empirical assessment of differential privacy in real-world observational data: a case-control study of asthma exacerbation in UK Biobank linked with electronic health records. [PDF]

J Am Med Inform Assoc
Mizani MA, Sheikh A, Banerjee A.
europepmc   +1 more source

Federated learning with differential privacy via fast Fourier transform for tighter-efficient combining. [PDF]

Sci Rep
Guo S, Yang J, Long S, Wang X, Liu G.
europepmc   +1 more source

Uldp-FL: Federated Learning with Across-Silo User-Level Differential Privacy. [PDF]

Proceedings VLDB Endowment
Kato F, Xiong L, Takagi S, Cao Y, Yoshikawa M.   +4 more
europepmc   +1 more source

DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy. [PDF]

Proc Int World Wide Web Conf
Zhang Q, Lee HK, Ma J, Lou J, Yang C, Xiong L.   +5 more
europepmc   +1 more source

Differential Privacy and Security [PDF]

Fundamenta Informaticae, 2016
A quantification of process’s security by differential privacy is defined and studied in the framework of probabilistic process algebras. The resulting (quantitative) security properties are investigated and compared with other (qualitative and quantitative) security notions.
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Differential Privacy for Databases

Foundations and Trends in Databases, 2021
Differential privacy is a promising approach to formalizing privacy—that is, for writing down what privacy means as a mathematical equation. This book is provides overview of differential privacy techniques for answering database-style queries. Within this area, we describe useful algorithms and their applications, and systems and tools that implement ...
Joseph P. Near, Xi He 0001
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Differential Privacy

2006
In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved.
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Differential Privacy in Practice

2012
Differential privacy (DP) has attracted considerable attention as the method of choice for releasing aggregate query results making it hard to infer information about individual records in the database. The most common way to achieve DP is to add noise following Laplace distribution.
Maryam Shoaran, Alex Thomo, Jens H. Weber   +2 more
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Differential privacy

Communications of the ACM, 2021
A discussion with Miguel Guevara, Damien Desfontaines, Jim Waldo, and Terry ...
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