Formalizing and Estimating Distribution Inference Risks

13 September 2021

Papers citing "Formalizing and Estimating Distribution Inference Risks"

37 / 37 papers shown

Title
On Linear Representations and Pretraining Data Frequency in Language Models Jack Merullo Noah A. Smith Sarah Wiegreffe Yanai Elazar 40 0 0 16 Apr 2025
Disparate Privacy Vulnerability: Targeted Attribute Inference Attacks and Defenses Ehsanul Kabir Lucas Craig Shagufta Mehnaz MIACV AAML 43 0 0 05 Apr 2025
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach Hanyang Yuan Jiarong Xu Renhong Huang Mingli Song Chunping Wang Yang Yang AAML 38 1 0 06 Nov 2024
Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning Jiaxin Li Marco Arazzi Antonino Nocera Mauro Conti 36 2 0 28 Sep 2024
Analyzing Inference Privacy Risks Through Gradients in Machine Learning Zhuohang Li Andrew Lowy Jing Liu T. Koike-Akino K. Parsons Bradley Malin Ye Wang FedML 38 1 0 29 Aug 2024
Range Membership Inference Attacks Jiashu Tao Reza Shokri 42 1 0 09 Aug 2024
Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data? J. Hayase Alisa Liu Yejin Choi Sewoong Oh Noah A. Smith 41 10 0 23 Jul 2024
Guarding Multiple Secrets: Enhanced Summary Statistic Privacy for Data Sharing Shuaiqi Wang Rongzhe Wei Mohsen Ghassemi Eleonora Kreacic Vamsi K. Potluru 35 1 0 22 May 2024
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks Sayedeh Leila Noorbakhsh Binghui Zhang Yuan Hong Binghui Wang AAML 25 8 0 04 Mar 2024
Do Membership Inference Attacks Work on Large Language Models? Michael Duan Anshuman Suri Niloofar Mireshghallah Sewon Min Weijia Shi Luke Zettlemoyer Yulia Tsvetkov Yejin Choi David E. Evans Hanna Hajishirzi MIALM 42 79 0 12 Feb 2024
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them Chao-Jung Liu Boxi Chen Wei Shao Chris Zhang Kelvin Wong Yi Zhang 29 3 0 22 Jan 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 47 2 0 07 Dec 2023
Transpose Attack: Stealing Datasets with Bidirectional Training Guy Amit Mosh Levy Yisroel Mirsky SILM AAML 41 0 0 13 Nov 2023
SoK: Memorization in General-Purpose Large Language Models Valentin Hartmann Anshuman Suri Vincent Bindschaedler David E. Evans Shruti Tople Robert West KELM LLMAG 21 20 0 24 Oct 2023
User Inference Attacks on Large Language Models Nikhil Kandpal Krishna Pillutla Alina Oprea Peter Kairouz Christopher A. Choquette-Choo Zheng Xu SILM AAML 44 15 0 13 Oct 2023
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning Harsh Chaudhari Giorgio Severi Alina Oprea Jonathan R. Ullman 31 5 0 05 Oct 2023
Attesting Distributional Properties of Training Data for Machine Learning Vasisht Duddu Anudeep Das Nora Khayata Hossein Yalame T. Schneider Nirmal Asokan 48 5 0 18 Aug 2023
PriSampler: Mitigating Property Inference of Diffusion Models Hailong Hu Jun Pang DiffM 20 3 0 08 Jun 2023
GrOVe: Ownership Verification of Graph Neural Networks using Embeddings Asim Waheed Vasisht Duddu Nadarajah Asokan 35 9 0 17 Apr 2023
Manipulating Transfer Learning for Property Inference Yulong Tian Fnu Suya Anshuman Suri Fengyuan Xu David E. Evans AAML 31 6 0 21 Mar 2023
Summary Statistic Privacy in Data Sharing Zinan Lin Shuaiqi Wang Vyas Sekar Giulia Fanti 43 7 0 03 Mar 2023
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning A. Salem Giovanni Cherubin David E. Evans Boris Köpf Andrew J. Paverd Anshuman Suri Shruti Tople Santiago Zanella Béguelin 47 35 0 21 Dec 2022
Dissecting Distribution Inference Anshuman Suri Yifu Lu Yanjin Chen David E. Evans 30 14 0 15 Dec 2022
Inferring Class Label Distribution of Training Data from Classifiers: An Accuracy-Augmented Meta-Classifier Attack Raksha Ramakrishna Gyorgy Dán 22 2 0 08 Nov 2022
Distribution inference risks: Identifying and mitigating sources of leakage Valentin Hartmann Léo Meynent Maxime Peyrard Dimitrios Dimitriadis Shruti Tople Robert West MIACV 29 14 0 18 Sep 2022
Black-Box Audits for Group Distribution Shifts Marc Juárez Samuel Yeom Matt Fredrikson MLAU 27 4 0 08 Sep 2022
Group Property Inference Attacks Against Graph Neural Networks Xiuling Wang Wendy Hui Wang AAML 27 30 0 02 Sep 2022
SNAP: Efficient Extraction of Private Properties with Poisoning Harsh Chaudhari John Abascal Alina Oprea Matthew Jagielski Florian Tramèr Jonathan R. Ullman MIACV 39 30 0 25 Aug 2022
Protecting Global Properties of Datasets with Distribution Privacy Mechanisms Michelle Chen O. Ohrimenko FedML 24 12 0 18 Jul 2022
Subject Membership Inference Attacks in Federated Learning Anshuman Suri Pallika H. Kanani Virendra J. Marathe Daniel W. Peterson 30 25 0 07 Jun 2022
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning Harsh Chaudhari Matthew Jagielski Alina Oprea 38 7 0 20 May 2022
Lessons Learned: Defending Against Property Inference Attacks Joshua Stock Jens Wettlaufer Daniel Demmler Hannes Federrath AAML 32 1 0 18 May 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 43 158 0 13 Jan 2022
Correlation inference attacks against machine learning models Ana-Maria Creţu Florent Guépin Yves-Alexandre de Montjoye MIACV AAML 38 5 0 16 Dec 2021
Formalizing Distribution Inference Risks Anshuman Suri David E. Evans MIACV AAML 20 4 0 07 Jun 2021
Dataset Inference: Ownership Resolution in Machine Learning Pratyush Maini Mohammad Yaghini Nicolas Papernot FedML 72 105 0 21 Apr 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 1,824 0 14 Dec 2020