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2112.02918
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When the Curious Abandon Honesty: Federated Learning Is Not Private
European Symposium on Security and Privacy (EuroS&P), 2021
6 December 2021
Franziska Boenisch
Adam Dziedzic
R. Schuster
Ali Shahin Shamsabadi
Ilia Shumailov
Nicolas Papernot
FedML
AAML
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Papers citing
"When the Curious Abandon Honesty: Federated Learning Is Not Private"
50 / 119 papers shown
Visual Privacy Auditing with Diffusion Models
Kristian Schwethelm
Johannes Kaiser
Moritz Knolle
Daniel Rueckert
Daniel Rueckert
Alexander Ziller
DiffM
AAML
241
2
0
12 Mar 2024
SPEAR:Exact Gradient Inversion of Batches in Federated Learning
Dimitar I. Dimitrov
Maximilian Baader
Mark Niklas Muller
Martin Vechev
FedML
215
16
0
06 Mar 2024
Analysis of Privacy Leakage in Federated Large Language Models
Minh Nhat Vu
Truc D. T. Nguyen
Tre' R. Jeter
My T. Thai
198
12
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
160
2
0
22 Feb 2024
From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
Anneliese Riess
Kristian Schwethelm
Johannes Kaiser
Tamara T. Mueller
Julia A. Schnabel
Daniel Rueckert
Alexander Ziller
MIACV
AAML
324
1
0
20 Feb 2024
Privacy Attacks in Decentralized Learning
Abdellah El Mrini
Edwige Cyffers
A. Bellet
410
9
0
15 Feb 2024
Data Reconstruction Attacks and Defenses: A Systematic Evaluation
Sheng Liu
Zihan Wang
Yuxiao Chen
Qi Lei
AAML
MIACV
483
6
0
13 Feb 2024
You Still See Me: How Data Protection Supports the Architecture of ML Surveillance
AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2024
Rui-Jie Yew
Lucy Qin
Suresh Venkatasubramanian
220
4
0
09 Feb 2024
Survey of Privacy Threats and Countermeasures in Federated Learning
M. Hayashitani
Junki Mori
Isamu Teranishi
FedML
377
1
0
01 Feb 2024
Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks
Lulu Xue
Shengshan Hu
Rui-Qing Zhao
Leo Yu Zhang
Shengqing Hu
Lichao Sun
Dezhong Yao
AAML
220
7
0
30 Jan 2024
Federated learning with distributed fixed design quantum chips and quantum channels
Ammar Daskin
FedML
275
0
0
24 Jan 2024
Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning
Jianwei Li
Sheng Liu
Qi Lei
PILM
SILM
AAML
254
4
0
10 Dec 2023
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
Alexander Ziller
Tamara T. Mueller
Simon Stieger
Leonhard F. Feiner
Johannes Brandt
R. Braren
Daniel Rueckert
Georgios Kaissis
204
1
0
05 Dec 2023
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
IEEE International Conference on Distributed Computing Systems (ICDCS), 2023
Tre' R. Jeter
Truc D. T. Nguyen
Raed Alharbi
My T. Thai
AAML
258
0
0
23 Nov 2023
Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2023
Feng Wang
Senem Velipasalar
M. C. Gursoy
180
3
0
30 Oct 2023
Robust and Actively Secure Serverless Collaborative Learning
Neural Information Processing Systems (NeurIPS), 2023
Olive Franzese
Adam Dziedzic
Christopher A. Choquette-Choo
Mark R. Thomas
Muhammad Ahmad Kaleem
Stephan Rabanser
Cong Fang
Somesh Jha
Nicolas Papernot
Xiao Wang
OOD
222
5
0
25 Oct 2023
Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering
Md Rafi Ur Rashid
Vishnu Asutosh Dasu
Kang Gu
Najrin Sultana
Shagufta Mehnaz
AAML
FedML
519
14
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
354
21
0
19 Oct 2023
Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning
IEEE Transactions on Dependable and Secure Computing (IEEE TDSC), 2023
Hongsheng Hu
Xuyun Zhang
Z. Salcic
Lichao Sun
K. Choo
Gillian Dobbie
178
28
0
30 Sep 2023
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
Martin Pelikan
Sheikh Shams Azam
Vitaly Feldman
Jan Honza Silovsky
Kunal Talwar
Christopher G. Brinton
Tatiana Likhomanenko
572
8
0
29 Sep 2023
Collaborative Distributed Machine Learning
ACM Computing Surveys (ACM Comput. Surv.), 2023
Sumit Kumar Jha
Patrick Lincoln
Sascha Rank
Ali Sunyaev
388
5
0
28 Sep 2023
Client-side Gradient Inversion Against Federated Learning from Poisoning
Jiaheng Wei
Yanjun Zhang
Leo Yu Zhang
Chao Chen
Shirui Pan
Kok-Leong Ong
Jinchao Zhang
Yang Xiang
AAML
162
5
0
14 Sep 2023
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
International Conference on Learning Representations (ICLR), 2023
Yavuz Faruk Bakman
D. Yaldiz
Yahya H. Ezzeldin
A. Avestimehr
CLL
FedML
417
23
0
03 Sep 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis
Conference on Computer and Communications Security (CCS), 2023
Kunal Talwar
Shan Wang
Audra McMillan
Vojta Jina
Vitaly Feldman
...
Congzheng Song
Karl Tarbe
Sebastian Vogt
L. Winstrom
Shundong Zhou
FedML
366
17
0
27 Jul 2023
SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning
International Conference on Machine Learning (ICML), 2023
Tanguy Marchand
Regis Loeb
Ulysse Marteau-Ferey
Jean Ogier du Terrail
Arthur Pignet
FedML
320
5
0
13 Jun 2023
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
Kostadin Garov
Dimitar I. Dimitrov
Nikola Jovanović
Martin Vechev
AAML
FedML
522
11
0
05 Jun 2023
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey
ACM Computing Surveys (ACM Comput. Surv.), 2023
Yanna Jiang
Baihe Ma
Xu Wang
Ping Yu
Guangsheng Yu
Zhe Wang
Weiquan Ni
R. Liu
AI4CE
229
51
0
08 May 2023
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
Computer Vision and Pattern Recognition (CVPR), 2023
Joshua C. Zhao
A. Elkordy
Atul Sharma
Yahya H. Ezzeldin
A. Avestimehr
S. Bagchi
FedML
138
15
0
27 Mar 2023
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation
IEEE Symposium on Security and Privacy (IEEE S&P), 2023
Joshua C. Zhao
Atul Sharma
A. Elkordy
Yahya H. Ezzeldin
Salman Avestimehr
S. Bagchi
AAML
FedML
182
53
0
21 Mar 2023
Manipulating Transfer Learning for Property Inference
Computer Vision and Pattern Recognition (CVPR), 2023
Yulong Tian
Fnu Suya
Anshuman Suri
Fengyuan Xu
David Evans
AAML
178
9
0
21 Mar 2023
Client-specific Property Inference against Secure Aggregation in Federated Learning
Raouf Kerkouche
G. Ács
Mario Fritz
FedML
263
12
0
07 Mar 2023
Active Membership Inference Attack under Local Differential Privacy in Federated Learning
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Truc D. T. Nguyen
Phung Lai
K. Tran
Nhathai Phan
My T. Thai
FedML
253
31
0
24 Feb 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning
IEEE Transactions on Emerging Topics in Computing (IEEE TETC), 2023
Van Tuan Tran
Huy Hieu Pham
Kok-Seng Wong
FedML
219
10
0
22 Feb 2023
WW-FL: Secure and Private Large-Scale Federated Learning
F. Marx
T. Schneider
Ajith Suresh
Tobias Wehrle
Christian Weinert
Hossein Yalame
FedML
398
5
0
20 Feb 2023
Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III
Aron N. Horvath
Matteo Berchier
Farhad Nooralahzadeh
Ahmed Allam
Michael Krauthammer
FedML
184
4
0
08 Feb 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Communications Medicine (Commun Med), 2023
Soroosh Tayebi Arasteh
Alexander Ziller
Christiane Kuhl
Marcus R. Makowski
S. Nebelung
R. Braren
Daniel Rueckert
Daniel Truhn
Georgios Kaissis
MedIm
504
32
0
03 Feb 2023
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation
European Symposium on Security and Privacy (Euro S&P), 2023
Franziska Boenisch
Adam Dziedzic
R. Schuster
Ali Shahin Shamsabadi
Ilia Shumailov
Nicolas Papernot
FedML
321
30
0
09 Jan 2023
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
International Conference on Machine Learning (ICML), 2022
Florian Tramèr
Gautam Kamath
Nicholas Carlini
SILM
400
96
0
13 Dec 2022
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction
European Symposium on Research in Computer Security (ESORICS), 2022
Mohamed Suliman
D. Leith
SILM
FedML
176
8
0
30 Oct 2022
Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses
ACM Computing Surveys (ACM CSUR), 2022
Adnan Qayyum
M. A. Butt
Hassan Ali
Muhammad Usman
O. Halabi
Ala I. Al-Fuqaha
Q. Abbasi
Muhammad Ali Imran
Junaid Qadir
234
59
0
24 Oct 2022
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
Ruihan Wu
Xiangyu Chen
Chuan Guo
Kilian Q. Weinberger
FedML
185
39
0
19 Oct 2022
ScionFL: Efficient and Robust Secure Quantized Aggregation
Y. Ben-Itzhak
Helen Mollering
Benny Pinkas
T. Schneider
Ajith Suresh
Oleksandr Tkachenko
S. Vargaftik
Christian Weinert
Hossein Yalame
Avishay Yanai
219
11
0
13 Oct 2022
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems
IEEE Transactions on Information Forensics and Security (IEEE TIFS), 2022
Jiahui Chen
Yi Zhao
Qi Li
Xuewei Feng
Ke Xu
AAML
FedML
314
29
0
08 Oct 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
International Conference on Learning Representations (ICLR), 2022
Samuel Maddock
Alexandre Sablayrolles
Pierre Stock
FedML
292
28
0
06 Oct 2022
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph
ACM Transactions on Privacy and Security (TOPS), 2022
Yang Lu
Zhengxin Yu
N. Suri
FedML
240
22
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
356
37
0
30 Sep 2022
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis
International Conference on Machine Learning (ICML), 2022
Sanjay Kariyappa
Chuan Guo
Kiwan Maeng
Wenjie Xiong
G. E. Suh
Moinuddin K. Qureshi
Hsien-Hsin S. Lee
FedML
184
41
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
229
37
0
25 Aug 2022
Verifiable Encodings for Secure Homomorphic Analytics
Sylvain Chatel
Christian Knabenhans
Apostolos Pyrgelis
Carmela Troncoso
Jean-Pierre Hubaux
302
24
0
28 Jul 2022
Data Leakage in Federated Averaging
Dimitar I. Dimitrov
Mislav Balunović
Nikola Konstantinov
Martin Vechev
FedML
281
38
0
24 Jun 2022
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