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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Federated Learning of User Authentication Models H. Hosseini Sungrack Yun Hyunsin Park Christos Louizos Joseph B. Soriaga Max Welling FedML 18 12 0 09 Jul 2020
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning Vaikkunth Mugunthan Ravi Rahman Lalana Kagal FedML 13 40 0 08 Jul 2020
Privacy Threats Against Federated Matrix Factorization Dashan Gao Ben Tan Ce Ju V. Zheng Qiang Yang 13 12 0 03 Jul 2020
Rotation-Equivariant Neural Networks for Privacy Protection Hao Zhang Yiting Chen Haotian Ma Xu Cheng Qihan Ren Liyao Xiang Jie Shi Quanshi Zhang 18 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 17 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 22 18 0 14 Jun 2020
Understanding Unintended Memorization in Federated Learning Om Thakkar Swaroop Indra Ramaswamy Rajiv Mathews Franccoise Beaufays FedML 14 45 0 12 Jun 2020
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging C. Sabater A. Bellet J. Ramon FedML 21 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 24 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 22 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 21 8 0 19 May 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 25 83 0 18 May 2020
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints Amir Sonee Stefano Rini 19 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 17 71 0 08 May 2020
When Machine Unlearning Jeopardizes Privacy Min Chen Zhikun Zhang Tianhao Wang Michael Backes Mathias Humbert Yang Zhang MIACV 31 218 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 73 25 0 27 Apr 2020
Enhancing Privacy via Hierarchical Federated Learning A. Wainakh Alejandro Sánchez Guinea Tim Grube M. Mühlhäuser FedML 28 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 18 146 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 18 175 0 12 Apr 2020
PrivEdge: From Local to Distributed Private Training and Prediction Ali Shahin Shamsabadi Adria Gascon Hamed Haddadi Andrea Cavallaro 26 19 0 12 Apr 2020
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies David Enthoven Zaid Al-Ars FedML 60 50 0 01 Apr 2020
Information Leakage in Embedding Models Congzheng Song A. Raghunathan MIACV 21 262 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 26 1,193 0 31 Mar 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 359 0 24 Mar 2020
Survey of Personalization Techniques for Federated Learning V. Kulkarni Milind Kulkarni Aniruddha Pant FedML 182 327 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 8 122 0 16 Mar 2020
Dynamic Backdoor Attacks Against Machine Learning Models A. Salem Rui Wen Michael Backes Shiqing Ma Yang Zhang AAML 39 270 0 07 Mar 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 202 434 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 25 11 0 29 Feb 2020
PrivacyFL: A simulator for privacy-preserving and secure federated learning Vaikkunth Mugunthan Anton Peraire-Bueno Lalana Kagal FedML 14 57 0 19 Feb 2020
Wireless Federated Learning with Local Differential Privacy Mohamed Seif Ravi Tandon Ming Li 84 171 0 12 Feb 2020
Salvaging Federated Learning by Local Adaptation Tao Yu Eugene Bagdasaryan Vitaly Shmatikov FedML 25 260 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 Ali Sunyaev 32 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 14 13 0 24 Jan 2020
iDLG: Improved Deep Leakage from Gradients Bo Zhao Konda Reddy Mopuri Hakan Bilen FedML 23 623 0 08 Jan 2020
Attack-Resistant Federated Learning with Residual-based Reweighting Shuhao Fu Chulin Xie Bo-wen Li Qifeng Chen FedML AAML 30 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 18 167 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 19 33 0 17 Dec 2019
Efficient Per-Example Gradient Computations in Convolutional Neural Networks G. Rochette Andre Manoel Eric W. Tramel 14 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 74 6,091 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 46 1,075 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 24 46 0 21 Nov 2019
Theoretical Guarantees for Model Auditing with Finite Adversaries Mario Díaz Peter Kairouz Jiachun Liao Lalitha Sankar MLAU AAML 34 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 14 30 0 06 Nov 2019
RIGA: Covert and Robust White-Box Watermarking of Deep Neural Networks Tianhao Wang Florian Kerschbaum AAML 19 36 0 31 Oct 2019
Communication-Efficient Local Decentralized SGD Methods Xiang Li Wenhao Yang Shusen Wang Zhihua Zhang 30 53 0 21 Oct 2019
Eavesdrop the Composition Proportion of Training Labels in Federated Learning Lixu Wang Shichao Xu Tianlin Li Qi Zhu FedML 20 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 14 54 0 12 Oct 2019