Measuring Data Leakage in Machine-Learning Models with Fisher
Information

Measuring Data Leakage in Machine-Learning Models with Fisher Information

23 February 2021

L. V. D. van der Maaten

Papers citing "Measuring Data Leakage in Machine-Learning Models with Fisher Information"

10 / 10 papers shown

Title
Quantifying Privacy Leakage in Split Inference via Fisher-Approximated Shannon Information Analysis Ruijun Deng Zhihui Lu Qiang Duan FedML 46 0 0 14 Apr 2025
Fundamental Limits of Membership Inference Attacks on Machine Learning Models Eric Aubinais Elisabeth Gassiat Pablo Piantanida MIACV 50 2 0 20 Oct 2023
How Do Input Attributes Impact the Privacy Loss in Differential Privacy? Tamara T. Mueller Stefan Kolek F. Jungmann Alexander Ziller Dmitrii Usynin Moritz Knolle Daniel Rueckert Georgios Kaissis 10 3 0 18 Nov 2022
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 33 7 0 08 Nov 2022
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano Chuan Guo Alexandre Sablayrolles Maziar Sanjabi FedML 29 17 0 24 Oct 2022
Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information Kiwan Maeng Chuan Guo Sanjay Kariyappa Ed Suh FedML 30 6 0 21 Sep 2022
Inferring Sensitive Attributes from Model Explanations Vasisht Duddu A. Boutet MIACV SILM 24 16 0 21 Aug 2022
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices Hansika Hewamalage Klaus Ackermann Christoph Bergmeir AI4TS 67 89 0 21 Mar 2022
SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning Vasisht Duddu S. Szyller Nadarajah Asokan 32 12 0 04 Dec 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 290 1,831 0 14 Dec 2020