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Measuring Data Leakage in Machine-Learning Models with Fisher Information

23 February 2021

Awni Y. Hannun

Chuan Guo

Laurens van der Maaten

FedML

MIACV

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

26 / 26 papers shown

Title
Breaking the Gaussian Barrier: Residual-PAC Privacy for Automatic Privatization Tao Zhang Yevgeniy Vorobeychik 28 0 0 06 Jun 2025
Practical Bayes-Optimal Membership Inference Attacks Marcus Lassila Johan Ostman K. Ngo Alexandre Graell i Amat 30 0 0 30 May 2025
Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AI Meghali Nandi Arash Shaghaghi Nazatul Haque Sultan Gustavo Batista Raymond K. Zhao Sanjay Jha AAML 172 0 0 16 May 2025
Quantifying Privacy Leakage in Split Inference via Fisher-Approximated Shannon Information Analysis Ruijun Deng Zhihui Lu Qiang Duan FedML 199 0 0 14 Apr 2025
SimClone: Detecting Tabular Data Clones using Value Similarity Xu Yang Gopi Krishnan Rajbahadur Dayi Lin Shaowei Wang Zhen Ming Jiang 70 1 0 24 Jun 2024
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds Kamalika Chaudhuri Chuan Guo Laurens van der Maaten Saeed Mahloujifar M. Tygert 44 1 0 03 Apr 2024
Fisher Mask Nodes for Language Model Merging Thennal D K Ganesh Nathan Suchithra M S MoMe AI4CE 97 5 0 14 Mar 2024
Privacy Amplification for the Gaussian Mechanism via Bounded Support Shengyuan Hu Saeed Mahloujifar Virginia Smith Kamalika Chaudhuri Chuan Guo FedML 72 1 0 07 Mar 2024
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach Qi Tan Qi Li Yi Zhao Zhuotao Liu Xiaobing Guo Ke Xu FedML 78 2 0 02 Mar 2024
SoK: Memorisation in machine learning Dmitrii Usynin Moritz Knolle Georgios Kaissis 92 1 0 06 Nov 2023
SparseLock: Securing Neural Network Models in Deep Learning Accelerators Nivedita Shrivastava S. Sarangi AAML 99 1 0 05 Nov 2023
Fundamental Limits of Membership Inference Attacks on Machine Learning Models Eric Aubinais Elisabeth Gassiat Pablo Piantanida MIACV 168 2 0 20 Oct 2023
Understanding Deep Gradient Leakage via Inversion Influence Functions Haobo Zhang Junyuan Hong Yuyang Deng M. Mahdavi Jiayu Zhou FedML 104 6 0 22 Sep 2023
Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning Lars Nieradzik Jördis Sieburg-Rockel Stephanie Helmling J. Keuper Thomas Weibel Andrea Olbrich Henrike Stephani 60 6 0 18 Jul 2023
Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla Galen Andrew Peter Kairouz H. B. McMahan Alina Oprea Sewoong Oh 91 23 0 29 May 2023
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information Kiwan Maeng Chuan Guo Sanjay Kariyappa G. E. Suh 75 8 0 06 May 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 80 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 82 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 69 17 0 24 Oct 2022
PAC Privacy: Automatic Privacy Measurement and Control of Data Processing Hanshen Xiao S. Devadas 104 12 0 07 Oct 2022
Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information Kiwan Maeng Chuan Guo Sanjay Kariyappa Ed Suh FedML 92 6 0 21 Sep 2022
Inferring Sensitive Attributes from Model Explanations Vasisht Duddu A. Boutet MIACV SILM 78 17 0 21 Aug 2022
Protecting Data from all Parties: Combining FHE and DP in Federated Learning Arnaud Grivet Sébert Renaud Sirdey Oana Stan Cédric Gouy-Pailler FedML 35 0 0 09 May 2022
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices Hansika Hewamalage Klaus Ackermann Christoph Bergmeir AI4TS 150 97 0 21 Mar 2022
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri Laurens van der Maaten 168 55 0 28 Jan 2022
SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning Vasisht Duddu S. Szyller Nadarajah Asokan 74 13 0 04 Dec 2021