Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

24 February 2017

Papers citing "Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning"

50 / 160 papers shown

Title
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions Alberto Blanco-Justicia J. Domingo-Ferrer Sergio Martínez David Sánchez Adrian Flanagan K. E. Tan FedML 18 110 0 12 Dec 2020
On Lightweight Privacy-Preserving Collaborative Learning for Internet of Things by Independent Random Projections Linshan Jiang Rui Tan Xin Lou Guosheng Lin 19 12 0 11 Dec 2020
This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models Patrick J. Tinsley A. Czajka Patrick Flynn CVBM GAN 33 39 0 10 Dec 2020
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective Jingwei Sun Ang Li Binghui Wang Huanrui Yang Hai Li Yiran Chen FedML 19 163 0 08 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 355 0 07 Dec 2020
Unleashing the Tiger: Inference Attacks on Split Learning Dario Pasquini G. Ateniese M. Bernaschi FedML 21 147 0 04 Dec 2020
Privacy-Preserving XGBoost Inference Xianrui Meng J. Feigenbaum 9 14 0 09 Nov 2020
Revolutionizing Medical Data Sharing Using Advanced Privacy Enhancing Technologies: Technical, Legal and Ethical Synthesis J. Scheibner J. Raisaro J. Troncoso-Pastoriza M. Ienca J. Fellay E. Vayena Jean-Pierre Hubaux 15 75 0 27 Oct 2020
Amnesiac Machine Learning Laura Graves Vineel Nagisetty Vijay Ganesh MU MIACV 16 245 0 21 Oct 2020
Feature Inference Attack on Model Predictions in Vertical Federated Learning Xinjian Luo Yuncheng Wu Xiaokui Xiao Beng Chin Ooi FedML AAML 11 218 0 20 Oct 2020
SAPAG: A Self-Adaptive Privacy Attack From Gradients Yijue Wang Jieren Deng Danyi Guo Chenghong Wang Xianrui Meng Hang Liu Caiwen Ding Sanguthevar Rajasekaran 4 35 0 14 Sep 2020
POSEIDON: Privacy-Preserving Federated Neural Network Learning Sinem Sav Apostolos Pyrgelis J. Troncoso-Pastoriza D. Froelicher Jean-Philippe Bossuat João Sá Sousa Jean-Pierre Hubaux FedML 11 153 0 01 Sep 2020
NoPeek: Information leakage reduction to share activations in distributed deep learning Praneeth Vepakomma Abhishek Singh O. Gupta Ramesh Raskar MIACV FedML 16 84 0 20 Aug 2020
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications Jihong Park S. Samarakoon Anis Elgabli Joongheon Kim M. Bennis Seong-Lyun Kim Mérouane Debbah 34 161 0 06 Aug 2020
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence Tianqing Zhu Dayong Ye Wei Wang Wanlei Zhou Philip S. Yu SyDa 34 125 0 05 Aug 2020
Data Poisoning Attacks Against Federated Learning Systems Vale Tolpegin Stacey Truex Mehmet Emre Gursoy Ling Liu FedML 23 638 0 16 Jul 2020
A Survey of Privacy Attacks in Machine Learning M. Rigaki Sebastian Garcia PILM AAML 30 213 0 15 Jul 2020
Free-rider Attacks on Model Aggregation in Federated Learning Yann Fraboni Richard Vidal Marco Lorenzi FedML 6 124 0 21 Jun 2020
Topology-aware Differential Privacy for Decentralized Image Classification Shangwei Guo Tianwei Zhang Guowen Xu Hanzhou Yu Tao Xiang Yang Liu 14 18 0 14 Jun 2020
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing T. Ryffel Pierre Tholoniat D. Pointcheval Francis R. Bach FedML 19 94 0 08 Jun 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 25 83 0 18 May 2020
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 73 25 0 27 Apr 2020
Local Differential Privacy based Federated Learning for Internet of Things Yang Zhao Jun Zhao Mengmeng Yang Teng Wang Ning Wang Lingjuan Lyu Dusit Niyato Kwok-Yan Lam 25 292 0 19 Apr 2020
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies David Enthoven Zaid Al-Ars FedML 57 50 0 01 Apr 2020
Semi-Federated Learning Zhikun Chen Daofeng Li Mingde Zhao Sihai Zhang Jinkang Zhu FedML 8 18 0 28 Mar 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 358 0 24 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
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
Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records Jianfei Cui He Zhu Hao Deng Ziwei Chen Dianbo Liu 15 33 0 25 Dec 2019
Assessing differentially private deep learning with Membership Inference Daniel Bernau Philip-William Grassal J. Robl Florian Kerschbaum MIACV FedML 18 23 0 24 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
Federated Learning with Bayesian Differential Privacy Aleksei Triastcyn Boi Faltings FedML 11 172 0 22 Nov 2019
Communication-Efficient Local Decentralized SGD Methods Xiang Li Wenhao Yang Shusen Wang Zhihua Zhang 22 53 0 21 Oct 2019
PPGAN: Privacy-preserving Generative Adversarial Network Yi Liu Jialiang Peng James J. Q. Yu Yi Wu 32 70 0 04 Oct 2019
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints Felix Sattler K. Müller Wojciech Samek FedML 40 965 0 04 Oct 2019
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis Jing Ma Qiuchen Zhang Jian Lou Joyce C. Ho Li Xiong Xiaoqian Jiang 27 44 0 26 Aug 2019
A Federated Learning Approach for Mobile Packet Classification Evita Bakopoulou Bálint Tillman A. Markopoulou 13 30 0 30 Jul 2019
On the Convergence of FedAvg on Non-IID Data Xiang Li Kaixuan Huang Wenhao Yang Shusen Wang Zhihua Zhang FedML 35 2,278 0 04 Jul 2019
Deep Leakage from Gradients Ligeng Zhu Zhijian Liu Song Han FedML 11 2,156 0 21 Jun 2019
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks Felipe A. Mejia Paul Gamble Z. Hampel-Arias M. Lomnitz Nina Lopatina Lucas Tindall M. Barrios SILM 19 18 0 15 Jun 2019
AutoGAN-based Dimension Reduction for Privacy Preservation Hung Nguyen Di Zhuang Pei-Yuan Wu Jerome Chang 14 33 0 27 Feb 2019
Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment Ziqi Yang E. Chang Zhenkai Liang MLAU 17 60 0 22 Feb 2019
XONN: XNOR-based Oblivious Deep Neural Network Inference M. Riazi Mohammad Samragh Hao Chen Kim Laine Kristin E. Lauter F. Koushanfar FedML GNN BDL 17 280 0 19 Feb 2019
Secure Federated Transfer Learning Yang Liu Yan Kang Chaoping Xing Tianjian Chen Qiang Yang FedML 6 119 0 08 Dec 2018
No Peek: A Survey of private distributed deep learning Praneeth Vepakomma Tristan Swedish Ramesh Raskar O. Gupta Abhimanyu Dubey SyDa FedML 22 99 0 08 Dec 2018
Three Tools for Practical Differential Privacy K. V. D. Veen Ruben Seggers Peter Bloem Giorgio Patrini 11 39 0 07 Dec 2018
Differentially Private Data Generative Models Qingrong Chen Chong Xiang Minhui Xue Bo-wen Li Nikita Borisov Dali Kaafar Haojin Zhu SyDa AAML 15 79 0 06 Dec 2018
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning Milad Nasr Reza Shokri Amir Houmansadr FedML MIACV AAML 13 243 0 03 Dec 2018
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning Zhibo Wang Mengkai Song Zhifei Zhang Yang Song Qian Wang Hairong Qi FedML 14 775 0 03 Dec 2018
Deep Learning Towards Mobile Applications Ji Wang Bokai Cao Philip S. Yu Lichao Sun Weidong Bao Xiaomin Zhu HAI 24 98 0 10 Sep 2018