Results 21 to 30 of about 1,939,766 (365)
Federated Learning With Differential Privacy: Algorithms and Performance Analysis [PDF]
IEEE Transactions on Information Forensics and Security, 2019 Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries.
Kang Wei +8 more
semanticscholar +1 more source
Internet Policy Review, 2019 This contribution provides a short introduction into the conceptual and socio-technical development of privacy. It identifies central issues that inform and structure current debates as well as transformations of privacy spurred by digital technology. In particular, it highlights central ambivalences of privacy between protection and de-politicization ...
Matzner, Tobias, Ochs, Carsten
openaire +3 more sources
Digital radiography image quality evaluation using various phantoms and software
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Purpose To investigate the effect of the exposure parameters on image quality (IQ) metrics of phantom images, obtained automatically using software or from visual evaluation. Methods Three commercial phantoms and a homemade phantom constructed according to the instructions given in the IAEA Human Health Series No.
Ioannis A. Tsalafoutas +4 more
wiley +1 more source
Rényi Differential Privacy [PDF]
IEEE Computer Security Foundations Symposium, 2017 We propose a natural relaxation of differential privacy based on the Rényi divergence. Closely related notions have appeared in several recent papers that analyzed composition of differentially private mechanisms. We argue that the useful analytical tool
Ilya Mironov
semanticscholar +1 more source
Transparent Privacy is Principled Privacy
Harvard Data Science Review, 2022 In a technical treatment, this article establishes the necessity of transparent privacy for drawing unbiased statistical inference for a wide range of scientific questions. Transparency is a distinct feature enjoyed by differential privacy: the probabilistic mechanism with which the data are privatized can be made public without sabotaging the privacy ...
openaire +3 more sources
LSGAN-AT: enhancing malware detector robustness against adversarial examples
Cybersecurity, 2021 Adversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME. AME quality is a key factor to the robustness enhancement.
Jianhua Wang +4 more
doaj +1 more source
The Algorithmic Foundations of Differential Privacy
Foundations and Trends® in Theoretical Computer Science, 2014 The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the ...
C. Dwork, Aaron Roth
semanticscholar +1 more source
Numerical Composition of Differential Privacy [PDF]
Neural Information Processing Systems, 2021 We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of \emph{privacy loss random variables} to quantify the privacy loss of DP algorithms.The ...
Sivakanth Gopi, Y. Lee, Lukas Wutschitz
semanticscholar +1 more source
Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
Cybersecurity, 2022 Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught with numerous attack surfaces throughout the FL execution. These attacks can not only cause models to fail in specific tasks, but also infer private information.
Pengrui Liu, Xiangrui Xu, Wei Wang
doaj +1 more source
L-diversity: privacy beyond k-anonymity
IEEE International Conference on Data Engineering, 2006 Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called \kappa-anonymity has gained popularity.
Ashwin Machanavajjhala +3 more
semanticscholar +1 more source