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Found 3 papers, 1 papers with code
A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses
Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without a significant drop in model utility.
An Empirical Analysis of Fairness Notions under Differential Privacy
Recent works have shown that selecting an optimal model architecture suited to the differential privacy setting is necessary to achieve the best possible utility for a given privacy budget using differentially private stochastic gradient descent (DP-SGD)(Tramer and Boneh 2020; Cheng et al. 2022).
Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data
Open data plays a fundamental role in the 21th century by stimulating economic growth and by enabling more transparent and inclusive societies.
Cryptography and Security Machine Learning
