Search Results for author:
Found 6 papers, 3 papers with code
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
Federated Learning (FL) is a data-minimization approach enabling collaborative model training across diverse clients with local data, avoiding direct data exchange.
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.
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning
In addition to the threats of illegitimate access to data through security breaches, machine learning models pose an additional privacy risk to the data by indirectly revealing about it through the model predictions and parameters.
On Adversarial Bias and the Robustness of Fair Machine Learning
Optimizing prediction accuracy can come at the expense of fairness.
Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models
It provides a measure of the potential leakage of a model given its structure, as a function of the model complexity and the size of the training set.
