Gradient Leakage Attack

Gradient leakage attacks exploit the information inadvertently revealed in model gradients shared during federated learning, enabling reconstruction of sensitive training data. Current research focuses on improving the efficiency of these attacks, developing defenses such as gradient perturbation and data obfuscation techniques, and analyzing the vulnerabilities of specific model architectures like transformers and convolutional neural networks. Understanding and mitigating gradient leakage is crucial for ensuring the privacy and security of federated learning systems, impacting the widespread adoption of this collaborative machine learning paradigm.

Papers

September 19, 2024

Data Poisoning and Leakage Analysis in Federated Learning
Wenqi Wei, Tiansheng Huang, Zachary Yahn, Anoop Singhal, Margaret Loper, Ling Liu
Federated Learning Poisoning Attack Data Poisoning Attack Data Poisoning Leakage Attack Gradient Leakage Attack

July 7, 2024

Gradient Diffusion: A Perturbation-Resilient Gradient Leakage Attack
Xuan Liu, Siqi Cai, Qihua Zhou, Song Guo, Ruibin Li, Kaiwei Lin
Gradient Leakage Gradient Leakage Attack Gradient Perturbation Gradient Guided Diffusion

April 15, 2024

On the Efficiency of Privacy Attacks in Federated Learning
Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang, Wenbin Zhang, Yanzhao Wu
High Efficiency Privacy Attack Attack Success Rate Gradient Leakage Attack

November 22, 2023

A Theoretical Insight into Attack and Defense of Gradient Leakage in Transformer
Chenyang Li, Zhao Song, Weixin Wang, Chiwun Yang
Transformer Based Differential Privacy Theoretical Analysis Gradient Leakage Gradient Attack Gradient Leakage Attack

September 8, 2023

Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
Daniel Scheliga, Patrick Mäder, Marco Seeland
Gradient Inversion Attack Privacy Preserving Federated Learning Gradient Leakage Gradient Leakage Attack Variational Bottleneck

May 10, 2023

Securing Distributed SGD against Gradient Leakage Threats
Wenqi Wei, Ling Liu, Jingya Zhou, Ka-Ho Chow, Yanzhao Wu
Differential Privacy Federated Learning Stochastic Gradient Descent Gradient Leakage Gradient Leakage Attack Differential Privacy Noise

May 6, 2023

Gradient Leakage Defense with Key-Lock Module for Federated Learning
Hanchi Ren, Jingjing Deng, Xianghua Xie, Xiaoke Ma, Jianfeng Ma
Privacy Preserving Machine Learning Gradient Leakage Gradient Leakage Attack Door Status

December 5, 2022

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
Mingyuan Fan, Cen Chen, Chengyu Wang, Xiaodan Li, Wenmeng Zhou, Jun Huang
Sensitive Data State of the Art Defense Leakage Attack Based Refiner Gradient Leakage Attack Data Refinement

October 22, 2022

Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning
Pretom Roy Ovi, Emon Dey, Nirmalya Roy, Aryya Gangopadhyay
Differential Privacy Mixed Precision Quantization Gradient Leakage Attack Differential Privacy Noise Privacy Inference

October 20, 2022

Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matching
Cangxiong Chen, Neill D. F. Campbell
Gradient Based Attack Linear System Gradient Matching Gradient Leakage Attack Training Data Leakage

June 10, 2022

Deep Leakage from Model in Federated Learning
Zihao Zhao, Mengen Luo, Wenbo Ding
Full Model Model Weight Private Training Model Gradient Gradient Leakage Attack Deep Leakage

June 1, 2022

Defense Against Gradient Leakage Attacks via Learning to Obscure Data
Yuxuan Wan, Han Xu, Xiaorui Liu, Jie Ren, Wenqi Fan, Jiliang Tang
PRIvacy Leakage Gradient Leakage Attack Privacy Preserving Learning

December 28, 2021

APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers
Jiahao Lu, Xi Sheryl Zhang, Tianli Zhao, Xiangyu He, Jian Cheng
Vision Transformer Privacy Policy PRIvacy Leakage Transformer Inference Gradient Leakage Gradient Leakage Attack

December 25, 2021

Gradient Leakage Attack Resilient Deep Learning
Wenqi Wei, Ling Liu
Differential Privacy Private Algorithm Differential Privacy Guarantee Gradient Leakage Gradient Leakage Attack

December 7, 2021

Location Leakage in Federated Signal Maps
Evita Bakopoulou, Mengwei Yang, Jiang Zhang, Konstantinos Psounis, Athina Markopoulou
Mobile User Cellular Network Gradient Leakage Attack Signal Map