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Reconstructing Individual Data Points in Federated Learning Hardened
  with Differential Privacy and Secure Aggregation

Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation

9 January 2023
Franziska Boenisch
Adam Dziedzic
R. Schuster
Ali Shahin Shamsabadi
Ilia Shumailov
Nicolas Papernot
    FedML
ArXivPDFHTML

Papers citing "Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation"

9 / 9 papers shown
Title
Visual Privacy Auditing with Diffusion Models
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
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation
Dzung Pham
Shreyas Kulkarni
Amir Houmansadr
33
0
0
29 Oct 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis
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
38
13
0
27 Jul 2023
Fishing for User Data in Large-Batch Federated Learning via Gradient
  Magnification
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
Yuxin Wen
Jonas Geiping
Liam H. Fowl
Micah Goldblum
Tom Goldstein
FedML
92
93
0
01 Feb 2022
When the Curious Abandon Honesty: Federated Learning Is Not Private
When the Curious Abandon Honesty: Federated Learning Is Not Private
Franziska Boenisch
Adam Dziedzic
R. Schuster
Ali Shahin Shamsabadi
Ilia Shumailov
Nicolas Papernot
FedML
AAML
71
181
0
06 Dec 2021
Manipulating SGD with Data Ordering Attacks
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
Practical and Private (Deep) Learning without Sampling or Shuffling
Practical and Private (Deep) Learning without Sampling or Shuffling
Peter Kairouz
Brendan McMahan
Shuang Song
Om Thakkar
Abhradeep Thakurta
Zheng Xu
FedML
182
194
0
26 Feb 2021
Amplification by Shuffling: From Local to Central Differential Privacy
  via Anonymity
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Ulfar Erlingsson
Vitaly Feldman
Ilya Mironov
A. Raghunathan
Kunal Talwar
Abhradeep Thakurta
150
420
0
29 Nov 2018
Prochlo: Strong Privacy for Analytics in the Crowd
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
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