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 / 354 papers shown

Title
Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning Hongsheng Hu Xuyun Zhang Z. Salcic Lichao Sun K. Choo Gillian Dobbie 18 16 0 30 Sep 2023
Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey Mahdi Alkaeed Adnan Qayyum Junaid Qadir 31 16 0 19 Sep 2023
A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learning Tanveer Khan Khoa Nguyen A. Michalas 23 9 0 15 Sep 2023
FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs Zhenheng Tang Yuxin Wang Xin He Longteng Zhang Xinglin Pan ... Rongfei Zeng Kaiyong Zhao S. Shi Bingsheng He Xiaowen Chu 44 30 0 03 Sep 2023
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond Filippo Galli Kangsoo Jung Sayan Biswas C. Palamidessi Tommaso Cucinotta FedML 31 10 0 01 Sep 2023
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks Indu Joshi Priya Upadhya Gaurav Kumar Nayak Peter Schuffler Nassir Navab AAML FedML 38 0 0 14 Aug 2023
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection João Vitorino Isabel Praça Eva Maia AAML 30 22 0 13 Aug 2023
FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks Ehsanul Kabir Zeyu Song Md. Rafi Ur Rashid Shagufta Mehnaz 32 7 0 10 Aug 2023
GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization Hao Fang Bin Chen Xuan Wang Zhi Wang Shutao Xia 57 32 0 09 Aug 2023
Label Inference Attacks against Node-level Vertical Federated GNNs Marco Arazzi Mauro Conti Stefanos Koffas Marina Krček Antonino Nocera S. Picek Jing Xu FedML AAML 26 1 0 04 Aug 2023
The Applicability of Federated Learning to Official Statistics Joshua Stock Oliver Hauke Julius Weissmann Hannes Federrath FedML 21 1 0 28 Jul 2023
Mitigating Cross-client GANs-based Attack in Federated Learning Hong Huang Xinyu Lei Tao Xiang AAML 55 1 0 25 Jul 2023
Security and Privacy Issues of Federated Learning J. Hasan 24 10 0 22 Jul 2023
PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks Shiwei Ding Lan Zhang Miao Pan Xiaoyong Yuan AAML 30 5 0 20 Jul 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 43 23 0 20 Jul 2023
Co(ve)rtex: ML Models as storage channels and their (mis-)applications Md Abdullah Al Mamun Quazi Mishkatul Alam Erfan Shayegani Pedram Zaree Ihsen Alouani Nael B. Abu-Ghazaleh 42 0 0 17 Jul 2023
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses G. Buzaglo Niv Haim Gilad Yehudai Gal Vardi Yakir Oz Yaniv Nikankin Michal Irani 34 10 0 04 Jul 2023
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off Huiqiang Chen Tianqing Zhu Tao Zhang Wanlei Zhou Philip S. Yu FedML 31 43 0 25 Jun 2023
Fairness and Privacy-Preserving in Federated Learning: A Survey Taki Hasan Rafi Faiza Anan Noor Tahmid Hussain Dong-Kyu Chae FedML 43 39 0 14 Jun 2023
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios Haochen Mei Gaolei Li Jun Wu Longfei Zheng SILM AAML 39 11 0 13 Jun 2023
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users Y. Rahulamathavan Charuka Herath Xiaolan Liu S. Lambotharan Carsten Maple 41 12 0 08 Jun 2023
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs Shanshan Han Baturalp Buyukates Zijian Hu Han Jin Weizhao Jin ... Qifan Zhang Yuhui Zhang Carlee Joe-Wong Salman Avestimehr Chaoyang He SILM 31 12 0 08 Jun 2023
Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models Virginia Fernandez Pedro Sanchez W. H. Pinaya Grzegorz Jacenków Sotirios A. Tsaftaris Jorge Cardoso 34 18 0 02 Jun 2023
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy Achintha Wijesinghe Songyang Zhang Zhi Ding FedML 32 7 0 19 May 2023
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources Suraj Rajendran Weishen Pan M. Sabuncu Yong Chen Jiayu Zhou Fei Wang 57 14 0 10 May 2023
Turning Privacy-preserving Mechanisms against Federated Learning Marco Arazzi Mauro Conti Antonino Nocera S. Picek AAML FedML 27 15 0 09 May 2023
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey Yanna Jiang Baihe Ma Xu Wang Ping Yu Guangsheng Yu Zhe Wang Weiquan Ni R. Liu AI4CE 36 20 0 08 May 2023
Gradient Leakage Defense with Key-Lock Module for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie Xiaoke Ma Jianfeng Ma FedML 37 2 0 06 May 2023
Reconstructing Training Data from Multiclass Neural Networks G. Buzaglo Niv Haim Gilad Yehudai Gal Vardi Michal Irani 33 4 0 05 May 2023
Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack Hideaki Takahashi Jingjing Liu Yang Liu FedML 37 11 0 22 Apr 2023
Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks Yunlong Mao Zexi Xin Zhenyu Li Jue Hong Qingyou Yang Sheng Zhong MIACV AAML 31 10 0 19 Apr 2023
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain Nazar Waheed A. Rehman Anushka Nehra Mahnoor Farooq Nargis Tariq M. Jan Fazlullah Khan Abeer Z. Alalmaie P. Nanda 18 11 0 16 Apr 2023
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review Siqi Li Pinyan Liu G. G. Nascimento Xinru Wang F. Leite ... Daniel Ting Hamed Haddadi M. Ong M. A. Peres Nan Liu 20 11 0 14 Apr 2023
A Game-theoretic Framework for Privacy-preserving Federated Learning Xiaojin Zhang Lixin Fan Si-Yi Wang Wenjie Li Kai Chen Qiang Yang FedML 29 4 0 11 Apr 2023
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense Yue-li Cui Syed Imran Ali Meerza Zhuohang Li Luyang Liu Jiaxin Zhang Jian-Dong Liu AAML FedML 34 4 0 11 Apr 2023
Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning R. Behnia Mohammadreza Ebrahimi Arman Riasi Sherman S. M. Chow B. Padmanabhan Thang Hoang 28 6 0 07 Apr 2023
Data Privacy Preservation on the Internet of Things Jaydip Sen S. Dasgupta 24 2 0 01 Apr 2023
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation Joshua C. Zhao Atul Sharma A. Elkordy Yahya H. Ezzeldin Salman Avestimehr S. Bagchi AAML FedML 38 28 0 21 Mar 2023
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System Weizhao Jin Yuhang Yao Shanshan Han Jiajun Gu Carlee Joe-Wong Srivatsan Ravi A. Avestimehr Chaoyang He FedML 24 50 0 20 Mar 2023
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups Ioannis Arapakis P. Papadopoulos Kleomenis Katevas Diego Perino 24 7 0 26 Feb 2023
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy Yifei Zhang Dun Zeng Jinglong Luo Zenglin Xu Irwin King FedML 84 48 0 21 Feb 2023
Digital Privacy Under Attack: Challenges and Enablers Baobao Song Mengyue Deng Shiva Raj Pokhrel Qiujun Lan R. Doss Gang Li AAML 39 3 0 18 Feb 2023
A Federated Approach for Hate Speech Detection Jay Gala Deep Gandhi Jash Mehta Zeerak Talat 21 4 0 18 Feb 2023
A Novel Noise Injection-based Training Scheme for Better Model Robustness Zeliang Zhang Jinyang Jiang Minjie Chen Zhiyuan Wang Yijie Peng Zhaofei Yu 30 3 0 17 Feb 2023
On the Privacy-Robustness-Utility Trilemma in Distributed Learning Youssef Allouah R. Guerraoui Nirupam Gupta Rafael Pinot John Stephan FedML 26 21 0 09 Feb 2023
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples Marco Jiralerspong A. Bose I. Gemp Chongli Qin Yoram Bachrach Gauthier Gidel EGVM 32 5 0 09 Feb 2023
Machine Learning for Synthetic Data Generation: A Review Ying-Cheng Lu Minjie Shen Huazheng Wang Xiao Wang Capucine Van Rechem Tianfan Fu Wenqi Wei SyDa 42 140 0 08 Feb 2023
GAN-based Vertical Federated Learning for Label Protection in Binary Classification Yujin Han Leying Guan FedML 35 0 0 04 Feb 2023
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations Hui-Po Wang Dingfan Chen Raouf Kerkouche Mario Fritz FedML DD 26 4 0 02 Feb 2023
Enforcing Privacy in Distributed Learning with Performance Guarantees Elsa Rizk Stefan Vlaski Ali H. Sayed FedML 30 9 0 16 Jan 2023