Results 21 to 30 of about 2,499,134 (251)
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
doaj +1 more source
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning [PDF]
ACM Computing Surveys, 2022 We review the use of differential privacy (DP) for privacy protection in machine learning (ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP-based ML implementations are so loose that they do not offer the ex ante ...
Alberto Blanco-Justicia +3 more
semanticscholar +1 more source
The Discrete Gaussian for Differential Privacy [PDF]
Neural Information Processing Systems, 2020 A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution presents several practical challenges. First and foremost, finite
C. Canonne, Gautam Kamath, T. Steinke
semanticscholar +1 more source
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey [PDF]
International Joint Conference on Artificial Intelligence, 2022 This paper surveys the recent work in the intersection of differential privacy (DP) and fairness. It focuses on surveying the work observing that DP systems may exacerbate bias and disparate impacts for different groups of individuals. The survey reviews
Ferdinando Fioretto +3 more
semanticscholar +1 more source
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy [PDF]
ACM-SIAM Symposium on Discrete Algorithms, 2022 The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19].
V. Feldman, Audra McMillan, Kunal Talwar
semanticscholar +1 more source
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
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
Differential Privacy for Deep and Federated Learning: A Survey
IEEE Access, 2022 Users’ privacy is vulnerable at all stages of the deep learning process. Sensitive information of users may be disclosed during data collection, during training, or even after releasing the trained learning model.
Ahmed El Ouadrhiri, Ahmed M Abdelhadi
semanticscholar +1 more source
Mixed Differential Privacy in Computer Vision [PDF]
Computer Vision and Pattern Recognition, 2022 We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data.
Aditya Golatkar +5 more
semanticscholar +1 more source
Composition of Differential Privacy & Privacy Amplification by Subsampling [PDF]
arXiv.org, 2022 This chapter is meant to be part of the book “Differential Privacy for Artificial Intelligence Applications.” We give an introduction to the most important property of differential privacy – composition: running multiple independent analyses on the data ...
T. Steinke
semanticscholar +1 more source