SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning

4 December 2021

Papers citing "SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning"

10 / 10 papers shown

Title
A Survey on Adversarial Machine Learning for Code Data: Realistic Threats, Countermeasures, and Interpretations Yulong Yang Haoran Fan Chenhao Lin Qian Li Zhengyu Zhao Chao Shen Xiaohong Guan AAML 43 0 0 12 Nov 2024
WaKA: Data Attribution using K-Nearest Neighbors and Membership Privacy Principles Patrick Mesana Clément Bénesse H. Lautraite Gilles Caporossi Sébastien Gambs TDI 32 0 0 02 Nov 2024
Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm? Rui Wen Michael Backes Yang Zhang TDI AAML 36 0 0 05 Sep 2024
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI Kaveen Hiniduma Suren Byna J. L. Bez Ravi Madduri 43 5 0 27 Jun 2024
Data Readiness for AI: A 360-Degree Survey Kaveen Hiniduma Suren Byna J. L. Bez 38 6 0 08 Apr 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 47 2 0 07 Dec 2023
SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark Jun Niu Xiaoyan Zhu Moxuan Zeng Ge Zhang Qingyang Zhao ... Peng Liu Yulong Shen Xiaohong Jiang Jianfeng Ma Yuqing Zhang 31 3 0 12 Jul 2023
On the Vulnerability of Data Points under Multiple Membership Inference Attacks and Target Models Mauro Conti Jiaxin Li S. Picek MIALM 28 2 0 28 Oct 2022
On the Robustness of Dataset Inference S. Szyller Rui Zhang Jian Liu Nadarajah Asokan AAML 15 6 0 24 Oct 2022
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 187 358 0 24 Mar 2020