When the Curious Abandon Honesty: Federated Learning Is Not Private

6 December 2021

Franziska Boenisch

Adam Dziedzic

R. Schuster

Ali Shahin Shamsabadi

Papers citing "When the Curious Abandon Honesty: Federated Learning Is Not Private"

41 / 41 papers shown

Title
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning Francesco Diana André Nusser Chuan Xu Giovanni Neglia 32 0 0 15 May 2025
Securing Genomic Data Against Inference Attacks in Federated Learning Environments Chetan Pathade Shubham Patil 36 0 0 12 May 2025
Controlled privacy leakage propagation throughout overlapping grouped learning Shahrzad Kiani Franziska Boenisch S. Draper FedML 79 0 0 06 Mar 2025
Privacy-Preserving Dataset Combination Keren Fuentes Mimee Xu Irene Chen 48 0 0 09 Feb 2025
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning Yiwei Zhang R. Behnia A. Yavuz Reza Ebrahimi E. Bertino FedML 38 2 0 13 Oct 2024
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy Kristian Schwethelm Johannes Kaiser Jonas Kuntzer Mehmet Yigitsoy Daniel Rueckert Georgios Kaissis 42 0 0 01 Oct 2024
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization Wenrui Yu Qiongxiu Li Milan Lopuhaä-Zwakenberg Mads Græsbøll Christensen Richard Heusdens FedML 40 4 0 12 Jul 2024
DAGER: Exact Gradient Inversion for Large Language Models Ivo Petrov Dimitar I. Dimitrov Maximilian Baader Mark Niklas Muller Martin Vechev FedML 68 3 0 24 May 2024
Confidential Federated Computations Hubert Eichner Daniel Ramage Kallista A. Bonawitz Dzmitry Huba Tiziano Santoro ... Albert Cheu Katharine Daly Adria Gascon Marco Gruteser Brendan McMahan 55 3 0 16 Apr 2024
Visual Privacy Auditing with Diffusion Models Kristian Schwethelm Johannes Kaiser Moritz Knolle Daniel Rueckert Daniel Rueckert Alexander Ziller DiffM AAML 40 0 0 12 Mar 2024
Analysis of Privacy Leakage in Federated Large Language Models Minh Nhat Vu Truc D. T. Nguyen Tre' R. Jeter My T. Thai 50 6 0 02 Mar 2024
Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs Anika Hannemann Jan Ewald Leo Seeger Erik Buchmann FedML 42 2 0 22 Feb 2024
Bounding Reconstruction Attack Success of Adversaries Without Data Priors Alexander Ziller Anneliese Riess Kristian Schwethelm Tamara T. Mueller Daniel Rueckert Georgios Kaissis MIACV AAML 44 1 0 20 Feb 2024
Data Reconstruction Attacks and Defenses: A Systematic Evaluation Sheng Liu Zihan Wang Yuxiao Chen Qi Lei AAML MIACV 61 4 0 13 Feb 2024
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering Md. Rafi Ur Rashid Vishnu Asutosh Dasu Kang Gu Najrin Sultana Shagufta Mehnaz AAML FedML 58 10 0 24 Oct 2023
PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretraining Kecen Li Chen Gong Zhixiang Li Yuzhong Zhao Xinwen Hou Tianhao Wang 38 10 0 19 Oct 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis Kunal Talwar Shan Wang Audra McMillan Vojta Jina Vitaly Feldman ... Congzheng Song Karl Tarbe Sebastian Vogt L. Winstrom Shundong Zhou FedML 43 13 0 27 Jul 2023
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning Joshua C. Zhao A. Elkordy Atul Sharma Yahya H. Ezzeldin A. Avestimehr S. Bagchi FedML 47 12 0 27 Mar 2023
Active Membership Inference Attack under Local Differential Privacy in Federated Learning Truc D. T. Nguyen Phung Lai K. Tran Nhathai Phan My T. Thai FedML 37 18 0 24 Feb 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning Van Tuan Tran Huy Hieu Pham Kok-Seng Wong FedML 46 7 0 22 Feb 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging Soroosh Tayebi Arasteh Alexander Ziller Christiane Kuhl Marcus R. Makowski S. Nebelung R. Braren Daniel Rueckert Daniel Truhn Georgios Kaissis MedIm 42 19 0 03 Feb 2023
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction Mohamed Suliman D. Leith SILM FedML 26 7 0 30 Oct 2022
Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses Adnan Qayyum M. A. Butt Hassan Ali Muhammad Usman O. Halabi Ala I. Al-Fuqaha Q. Abbasi Muhammad Ali Imran Junaid Qadir 35 32 0 24 Oct 2022
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning Ruihan Wu Xiangyu Chen Chuan Guo Kilian Q. Weinberger FedML 20 26 0 19 Oct 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning Samuel Maddock Alexandre Sablayrolles Pierre Stock FedML 39 22 0 06 Oct 2022
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph Yang Lu Zhengxin Yu N. Suri FedML 34 14 0 01 Oct 2022
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 37 31 0 30 Sep 2022
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis Sanjay Kariyappa Chuan Guo Kiwan Maeng Wenjie Xiong G. E. Suh Moinuddin K. Qureshi Hsien-Hsin S. Lee FedML 31 29 0 12 Sep 2022
SNAP: Efficient Extraction of Private Properties with Poisoning Harsh Chaudhari John Abascal Alina Oprea Matthew Jagielski Florian Tramèr Jonathan R. Ullman MIACV 44 30 0 25 Aug 2022
Verifiable Encodings for Secure Homomorphic Analytics Sylvain Chatel Christian Knabenhans Apostolos Pyrgelis Carmela Troncoso Jean-Pierre Hubaux 38 19 0 28 Jul 2022
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning Sikha Pentyala Nicola Neophytou A. Nascimento Martine De Cock G. Farnadi 47 17 0 23 May 2022
Recovering Private Text in Federated Learning of Language Models Samyak Gupta Yangsibo Huang Zexuan Zhong Tianyu Gao Kai Li Danqi Chen FedML 40 75 0 17 May 2022
Symbolic analysis meets federated learning to enhance malware identifier Khanh-Huu-The Dam Charles-Henry Bertrand Van Ouytsel Axel Legay FedML 34 5 0 29 Apr 2022
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets Florian Tramèr Reza Shokri Ayrton San Joaquin Hoang Minh Le Matthew Jagielski Sanghyun Hong Nicholas Carlini MIACV 59 111 0 31 Mar 2022
Sniper Backdoor: Single Client Targeted Backdoor Attack in Federated Learning Gorka Abad Servio Paguada Oguzhan Ersoy S. Picek Víctor Julio Ramírez-Durán A. Urbieta FedML 33 6 0 16 Mar 2022
FLAME: Federated Learning Across Multi-device Environments Hyunsung Cho Akhil Mathur F. Kawsar 16 21 0 17 Feb 2022
Preserving Privacy and Security in Federated Learning Truc D. T. Nguyen My T. Thai FedML 26 49 0 07 Feb 2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen Jonas Geiping Liam H. Fowl Micah Goldblum Tom Goldstein FedML 94 93 0 01 Feb 2022
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models Liam H. Fowl Jonas Geiping W. Czaja Micah Goldblum Tom Goldstein FedML 38 145 0 25 Oct 2021
Manipulating SGD with Data Ordering Attacks Ilia Shumailov Zakhar Shumaylov Dmitry Kazhdan Yiren Zhao Nicolas Papernot Murat A. Erdogdu Ross J. Anderson AAML 112 91 0 19 Apr 2021
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 91 278 0 02 Oct 2017