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Found 6 papers, 3 papers with code
Leave-one-out Distinguishability in Machine Learning
We introduce an analytical framework to quantify the changes in a machine learning algorithm's output distribution following the inclusion of a few data points in its training set, a notion we define as leave-one-out distinguishability (LOOD).
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning
Repeated parameter sharing in federated learning causes significant information leakage about private data, thus defeating its main purpose: data privacy.
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
We prove that, in these settings, our privacy bound converges exponentially fast and is substantially smaller than the composition bounds, notably after a few number of training epochs.
Enhanced Membership Inference Attacks against Machine Learning Models
Membership inference attacks are used as an auditing tool to quantify this leakage.
Privacy Auditing of Machine Learning using Membership Inference Attacks
In this paper, we present a framework that can explain the implicit assumptions and also the simplifications made in the prior work.
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
What is the information leakage of an iterative randomized learning algorithm about its training data, when the internal state of the algorithm is \emph{private}?
