38 citations · 97 across the 5 of their papers we have counts for
5 papers
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
Aydin Abadi, Bradley Doyle, Francesco Gini +9
Federated Learning (FL) is a data-minimization approach enabling collaborative model training across diverse clients with local data, avoiding direct data exchange. However, state-…
Enhanced Membership Inference Attacks against Machine Learning Models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda +2
How much does a machine learning algorithm leak about its training data, and why? Membership inference attacks are used as an auditing tool to quantify this leakage. In this paper,…
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning
Sasi Kumar Murakonda, Reza Shokri
When building machine learning models using sensitive data, organizations should ensure that the data processed in such systems is adequately protected. For projects involving mach…
On Adversarial Bias and the Robustness of Fair Machine Learning
Hongyan Chang, Ta Duy Nguyen, Sasi Kumar Murakonda +2
Optimizing prediction accuracy can come at the expense of fairness. Towards minimizing discrimination against a group, fair machine learning algorithms strive to equalize the behav…
Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models
Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos
Models leak information about their training data. This enables attackers to infer sensitive information about their training sets, notably determine if a data sample was part of t…