When Machine Learning Meets Privacy: A Survey and Outlook

24 November 2020

Ming Ding

Papers citing "When Machine Learning Meets Privacy: A Survey and Outlook"

24 / 24 papers shown

Title
A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks Hengzhu Liu Ping Xiong Tianqing Zhu Philip S. Yu 42 6 0 10 Jun 2024
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning Michael Duchesne Kaiwen Zhang Talhi Chamseddine FedML 34 0 0 20 Apr 2024
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems Guangjing Wang Ce Zhou Yuanda Wang Bocheng Chen Hanqing Guo Qiben Yan AAML SILM 68 3 0 20 Nov 2023
Decentralized Personalized Online Federated Learning Renzhi Wu Saayan Mitra Xiang Chen Anup Rao FedML 29 2 0 08 Nov 2023
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications Yi Zhang Yuying Zhao Zhaoqing Li Xueqi Cheng Yu-Chiang Frank Wang Olivera Kotevska Philip S. Yu Tyler Derr 26 10 0 31 Aug 2023
(Local) Differential Privacy has NO Disparate Impact on Fairness Héber H. Arcolezi K. Makhlouf C. Palamidessi 40 6 0 25 Apr 2023
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning A. Salem Giovanni Cherubin David Evans Boris Köpf Andrew J. Paverd Anshuman Suri Shruti Tople Santiago Zanella Béguelin 47 35 0 21 Dec 2022
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity Faisal Hamman Jiahao Chen Sanghamitra Dutta 25 9 0 03 Nov 2022
Differential Privacy has Bounded Impact on Fairness in Classification Paul Mangold Michaël Perrot A. Bellet Marc Tommasi 31 17 0 28 Oct 2022
Uncertainty-aware Personal Assistant for Making Personalized Privacy Decisions Gonul Ayci Murat Sensoy Arzucan Özgür P. Yolum 25 13 0 13 May 2022
MixNN: A design for protecting deep learning models Chao Liu Hao Chen Yusen Wu Rui Jin 10 0 0 28 Mar 2022
Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations Tao Guo Song Guo Jiewei Zhang Wenchao Xu Junxiao Wang MU 27 17 0 27 Feb 2022
Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges Huaming Chen Muhammad Ali Babar AAML 37 21 0 12 Jan 2022
Adversarial Attacks Against Deep Generative Models on Data: A Survey Hui Sun Tianqing Zhu Zhiqiu Zhang Dawei Jin Wanlei Zhou AAML 39 42 0 01 Dec 2021
Addressing Privacy Threats from Machine Learning Mary Anne Smart 26 2 0 25 Oct 2021
Predicting Patch Correctness Based on the Similarity of Failing Test Cases Haoye Tian Yinghua Li Weiguo Pian Abdoul Kader Kaboré Kui Liu Andrew Habib Jacques Klein Tegawende F. Bissyande 39 29 0 28 Jul 2021
Meta-learning Amidst Heterogeneity and Ambiguity Kyeongryeol Go Seyoung Yun 32 1 0 05 Jul 2021
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 23 71 0 04 Jul 2021
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 35 412 0 14 Mar 2021
DP-Image: Differential Privacy for Image Data in Feature Space Hanyu Xue Bo Liu Ming Ding Tianqing Zhu Dayong Ye Li-Na Song Wanlei Zhou 15 33 0 12 Mar 2021
A Distributed Privacy-Preserving Learning Dynamics in General Social Networks Youming Tao Shuzhen Chen Feng Li Dongxiao Yu Jiguo Yu Hao Sheng FedML 19 3 0 15 Nov 2020
Data Synthesis based on Generative Adversarial Networks Noseong Park Mahmoud Mohammadi Kshitij Gorde S. Jajodia Hongkyu Park Youngmin Kim 119 469 0 09 Jun 2018
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks Edward Choi Siddharth Biswal B. Malin J. Duke Walter F. Stewart Jimeng Sun SyDa GAN 156 569 0 19 Mar 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016