Exploiting Unintended Feature Leakage in Collaborative Learning

10 May 2018

Luca Melis

Congzheng Song

Emiliano De Cristofaro

Vitaly Shmatikov

FedML

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Papers citing "Exploiting Unintended Feature Leakage in Collaborative Learning"

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Rotation-Equivariant Neural Networks for Privacy Protection Hao Zhang Yiting Chen Haotian Ma Xu Cheng Qihan Ren Liyao Xiang Jie Shi Quanshi Zhang 13 3 0 21 Jun 2020
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks Lixin Fan Kam Woh Ng Ce Ju Tianyu Zhang Chang Liu Chee Seng Chan Qiang Yang MIACV 9 63 0 20 Jun 2020
Topology-aware Differential Privacy for Decentralized Image Classification Shangwei Guo Tianwei Zhang Guowen Xu Hanzhou Yu Tao Xiang Yang Liu 6 18 0 14 Jun 2020
Understanding Unintended Memorization in Federated Learning Om Thakkar Swaroop Indra Ramaswamy Rajiv Mathews Franccoise Beaufays FedML 11 45 0 12 Jun 2020
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging C. Sabater A. Bellet J. Ramon FedML 8 18 0 12 Jun 2020
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data Chengxu Yang Qipeng Wang Mengwei Xu Shangguang Wang Kaigui Bian Yunxin Liu Xuanzhe Liu 17 22 0 12 Jun 2020
Scalable Privacy-Preserving Distributed Learning D. Froelicher J. Troncoso-Pastoriza Apostolos Pyrgelis Sinem Sav João Sá Sousa Jean-Philippe Bossuat Jean-Pierre Hubaux FedML 6 68 0 19 May 2020
Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy Bashir Rastegarpanah M. Crovella Krishna P. Gummadi FaML 8 8 0 19 May 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 19 83 0 18 May 2020
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints Amir Sonee Stefano Rini 6 16 0 15 May 2020
Defending Model Inversion and Membership Inference Attacks via Prediction Purification Ziqi Yang Bin Shao Bohan Xuan E. Chang Fan Zhang AAML 9 71 0 08 May 2020
When Machine Unlearning Jeopardizes Privacy Min Chen Zhikun Zhang Tianhao Wang Michael Backes Mathias Humbert Yang Zhang MIACV 15 214 0 05 May 2020
Differentially Private Federated Learning with Laplacian Smoothing Zhicong Liang Bao Wang Quanquan Gu Stanley Osher Yuan Yao FedML 12 7 0 01 May 2020
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 60 25 0 27 Apr 2020
Enhancing Privacy via Hierarchical Federated Learning A. Wainakh Alejandro Sánchez Guinea Tim Grube M. Mühlhäuser FedML 12 45 0 23 Apr 2020
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning Wenqi Wei Ling Liu Margaret Loper Ka-Ho Chow Mehmet Emre Gursoy Stacey Truex Yanzhao Wu FedML 10 146 0 22 Apr 2020
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments Fan Mo Ali Shahin Shamsabadi Kleomenis Katevas Soteris Demetriou Ilias Leontiadis Andrea Cavallaro Hamed Haddadi FedML 8 175 0 12 Apr 2020
PrivEdge: From Local to Distributed Private Training and Prediction Ali Shahin Shamsabadi Adria Gascon Hamed Haddadi Andrea Cavallaro 18 19 0 12 Apr 2020
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies David Enthoven Zaid Al-Ars FedML 52 50 0 01 Apr 2020
Information Leakage in Embedding Models Congzheng Song A. Raghunathan MIACV 16 260 0 31 Mar 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 9 1,192 0 31 Mar 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 187 358 0 24 Mar 2020
Survey of Personalization Techniques for Federated Learning V. Kulkarni Milind Kulkarni Aniruddha Pant FedML 171 324 0 19 Mar 2020
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training? Sharif Abuadbba Kyuyeon Kim Minki Kim Chandra Thapa S. Çamtepe Yansong Gao Hyoungshick Kim Surya Nepal FedML 6 122 0 16 Mar 2020
Dynamic Backdoor Attacks Against Machine Learning Models A. Salem Rui Wen Michael Backes Shiqing Ma Yang Zhang AAML 16 269 0 07 Mar 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 191 433 0 04 Mar 2020
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization Kang Wei Jun Li Ming Ding Chuan Ma Hang Su Bo-Wen Zhang H. Vincent Poor FedML 19 11 0 29 Feb 2020
PrivacyFL: A simulator for privacy-preserving and secure federated learning Vaikkunth Mugunthan Anton Peraire-Bueno Lalana Kagal FedML 6 57 0 19 Feb 2020
Wireless Federated Learning with Local Differential Privacy Mohamed Seif Ravi Tandon Ming Li 68 171 0 12 Feb 2020
Salvaging Federated Learning by Local Adaptation Tao Yu Eugene Bagdasaryan Vitaly Shmatikov FedML 6 260 0 12 Feb 2020
On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda Konstantin D. Pandl Scott Thiebes Manuel Schmidt-Kraepelin A. Sunyaev 22 69 0 29 Jan 2020
Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack Bo Zhang Ruotong Yu Haipei Sun Yanying Li Jun Xu Wendy Hui Wang AAML 6 13 0 24 Jan 2020
iDLG: Improved Deep Leakage from Gradients Bo-Lu Zhao Konda Reddy Mopuri Hakan Bilen FedML 15 622 0 08 Jan 2020
Attack-Resistant Federated Learning with Residual-based Reweighting Shuhao Fu Chulin Xie Bo-wen Li Qifeng Chen FedML AAML 22 93 0 24 Dec 2019
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer Hong Chang Virat Shejwalkar Reza Shokri Amir Houmansadr FedML 10 167 0 24 Dec 2019
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud Shuang Zhang Liyao Xiang Congcong Li Yixuan Wang Quanshi Zhang Zeyu Liu Bo-wen Li FedML 8 1 0 18 Dec 2019
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence Yanan Li Shusen Yang Xuebin Ren Cong Zhao FedML 14 33 0 17 Dec 2019
Efficient Per-Example Gradient Computations in Convolutional Neural Networks G. Rochette Andre Manoel Eric W. Tramel 6 19 0 12 Dec 2019
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 57 6,057 0 10 Dec 2019
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning Minghong Fang Xiaoyu Cao Jinyuan Jia Neil Zhenqiang Gong AAML OOD FedML 26 1,069 0 26 Nov 2019
Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability Stacey Truex Ling Liu Mehmet Emre Gursoy Wenqi Wei Lei Yu MIACV 19 46 0 21 Nov 2019
Theoretical Guarantees for Model Auditing with Finite Adversaries Mario Díaz Peter Kairouz Jiachun Liao Lalitha Sankar MLAU AAML 18 2 0 08 Nov 2019
Secure Federated Submodel Learning Chaoyue Niu Fan Wu Shaojie Tang Lifeng Hua Rongfei Jia Chengfei Lv Zhihua Wu Guihai Chen FedML 6 30 0 06 Nov 2019
RIGA: Covert and Robust White-Box Watermarking of Deep Neural Networks Tianhao Wang Florian Kerschbaum AAML 11 36 0 31 Oct 2019
Communication-Efficient Local Decentralized SGD Methods Xiang Li Wenhao Yang Shusen Wang Zhihua Zhang 14 53 0 21 Oct 2019
Eavesdrop the Composition Proportion of Training Labels in Federated Learning Lixu Wang Shichao Xu Xiao Wang Qi Zhu FedML 12 62 0 14 Oct 2019
A blockchain-orchestrated Federated Learning architecture for healthcare consortia Jonathan Passerat-Palmbach Tyler Farnan Robert C Miller M. Gross H. Flannery Bill Gleim FedML 6 54 0 12 Oct 2019
Quantification of the Leakage in Federated Learning Zhaorui Li Zhicong Huang Chaochao Chen Cheng Hong FedML PILM 8 22 0 12 Oct 2019
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints Felix Sattler K. Müller Wojciech Samek FedML 17 964 0 04 Oct 2019
GAMIN: An Adversarial Approach to Black-Box Model Inversion Ulrich Aivodji Sébastien Gambs Timon Ther MLAU 17 42 0 26 Sep 2019