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CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated
  Learning

CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning

6 October 2022
Samuel Maddock
Alexandre Sablayrolles
Pierre Stock
    FedML
ArXivPDFHTML

Papers citing "CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning"

22 / 22 papers shown
Title
MCMC for Bayesian estimation of Differential Privacy from Membership Inference Attacks
MCMC for Bayesian estimation of Differential Privacy from Membership Inference Attacks
Ceren Yildirim
Kamer Kaya
Sinan Yildirim
Erkay Savas
31
0
0
23 Apr 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios
Sangyeon Yoon
Wonje Jeung
Albert No
85
0
0
02 Dec 2024
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
Zhuohang Li
Andrew Lowy
Jing Liu
T. Koike-Akino
K. Parsons
Bradley Malin
Ye Wang
FedML
38
1
0
29 Aug 2024
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
Tudor Cebere
A. Bellet
Nicolas Papernot
30
9
0
23 May 2024
Nearly Tight Black-Box Auditing of Differentially Private Machine
  Learning
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
Meenatchi Sundaram Muthu Selva Annamalai
Emiliano De Cristofaro
44
11
0
23 May 2024
"What do you want from theory alone?" Experimenting with Tight Auditing
  of Differentially Private Synthetic Data Generation
"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation
Meenatchi Sundaram Muthu Selva Annamalai
Georgi Ganev
Emiliano De Cristofaro
37
9
0
16 May 2024
Revealing the True Cost of Locally Differentially Private Protocols: An
  Auditing Perspective
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective
Héber H. Arcolezi
Sébastien Gambs
37
1
0
04 Sep 2023
Epsilon*: Privacy Metric for Machine Learning Models
Epsilon*: Privacy Metric for Machine Learning Models
Diana M. Negoescu
H. González
Saad Eddin Al Orjany
Jilei Yang
Yuliia Lut
...
Xinyi Zheng
Zachariah Douglas
Vidita Nolkha
P. Ahammad
G. Samorodnitsky
35
2
0
21 Jul 2023
Gaussian Membership Inference Privacy
Gaussian Membership Inference Privacy
Tobias Leemann
Martin Pawelczyk
Gjergji Kasneci
22
15
0
12 Jun 2023
Unleashing the Power of Randomization in Auditing Differentially Private
  ML
Unleashing the Power of Randomization in Auditing Differentially Private ML
Krishna Pillutla
Galen Andrew
Peter Kairouz
H. B. McMahan
Alina Oprea
Sewoong Oh
30
20
0
29 May 2023
Privacy Auditing with One (1) Training Run
Privacy Auditing with One (1) Training Run
Thomas Steinke
Milad Nasr
Matthew Jagielski
35
77
0
15 May 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential
  Privacy
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva
Hussein Hazimeh
Alexey Kurakin
Zheng Xu
Carson E. Denison
H. B. McMahan
Sergei Vassilvitskii
Steve Chien
Abhradeep Thakurta
94
167
0
01 Mar 2023
Tight Auditing of Differentially Private Machine Learning
Tight Auditing of Differentially Private Machine Learning
Milad Nasr
Jamie Hayes
Thomas Steinke
Borja Balle
Florian Tramèr
Matthew Jagielski
Nicholas Carlini
Andreas Terzis
FedML
35
52
0
15 Feb 2023
One-shot Empirical Privacy Estimation for Federated Learning
One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew
Peter Kairouz
Sewoong Oh
Alina Oprea
H. B. McMahan
Vinith M. Suriyakumar
FedML
27
32
0
06 Feb 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
89
92
0
01 Feb 2022
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning
  for Language Models
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
Liam H. Fowl
Jonas Geiping
Steven Reich
Yuxin Wen
Wojtek Czaja
Micah Goldblum
Tom Goldstein
FedML
73
56
0
29 Jan 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
69
181
0
06 Dec 2021
Papaya: Practical, Private, and Scalable Federated Learning
Papaya: Practical, Private, and Scalable Federated Learning
Dzmitry Huba
John Nguyen
Kshitiz Malik
Ruiyu Zhu
Michael G. Rabbat
...
H. Srinivas
Kaikai Wang
Anthony Shoumikhin
Jesik Min
Mani Malek
FedML
110
137
0
08 Nov 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour
I. Shilov
Alexandre Sablayrolles
Davide Testuggine
Karthik Prasad
...
Sayan Gosh
Akash Bharadwaj
Jessica Zhao
Graham Cormode
Ilya Mironov
VLM
159
350
0
25 Sep 2021
Gradient-based Adversarial Attacks against Text Transformers
Gradient-based Adversarial Attacks against Text Transformers
Chuan Guo
Alexandre Sablayrolles
Hervé Jégou
Douwe Kiela
SILM
100
227
0
15 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
Extracting Training Data from Large Language Models
Extracting Training Data from Large Language Models
Nicholas Carlini
Florian Tramèr
Eric Wallace
Matthew Jagielski
Ariel Herbert-Voss
...
Tom B. Brown
D. Song
Ulfar Erlingsson
Alina Oprea
Colin Raffel
MLAU
SILM
290
1,815
0
14 Dec 2020
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