1. TYLER LEBLOND ♂
Information acquired: 2025
TYLER LEBLOND is an academic who published a paper in 2022 entitled A General Framework for Auditing Differentially Private Machine Learning. TYLER LEBLOND published this work as part of a team: Lu, Fred. Here is a description of this work: We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward evaluating privacy loss through poisoning attacks or membership inference, they have been tailored to specific models or have demonstrated low statistical power. Our work develops a general methodology to empirically evaluate the privacy of differentially private machine learning implementations, combining improved privacy search and verification methods with a toolkit of influence-based poisoning attacks. We demonstrate significantly improved auditing power over previous approaches on a variety of models including logistic regression, Naive Bayes, and random forest. Our method can be used to detect privacy violations due to implementation errors or misuse. When violations are.
