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Nearly Tight Black-Box Auditing of Differentially Private Machine
  Learning

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning

23 May 2024
Meenatchi Sundaram Muthu Selva Annamalai
Emiliano De Cristofaro
ArXivPDFHTML

Papers citing "Nearly Tight Black-Box Auditing of Differentially Private Machine Learning"

12 / 12 papers shown
Title
DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
Chen Gong
Kecen Li
Zinan Lin
Tianhao Wang
61
3
0
18 Mar 2025
Empirical Privacy Variance
Empirical Privacy Variance
Yuzheng Hu
Fan Wu
Ruicheng Xian
Yuhang Liu
Lydia Zakynthinou
Pritish Kamath
Chiyuan Zhang
David A. Forsyth
62
0
0
16 Mar 2025
Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing
Debabrota Basu
Debarshi Chanda
41
0
0
10 Mar 2025
Privacy Auditing of Large Language Models
Ashwinee Panda
Xinyu Tang
Milad Nasr
Christopher A. Choquette-Choo
Prateek Mittal
PILM
62
5
0
09 Mar 2025
General-Purpose fff-DP Estimation and Auditing in a Black-Box Setting
Önder Askin
Holger Dette
Martin Dunsche
T. Kutta
Yun Lu
Yu Wei
Vassilis Zikas
52
0
0
10 Feb 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
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
Thomas Steinke
Milad Nasr
Arun Ganesh
Borja Balle
Christopher A. Choquette-Choo
Matthew Jagielski
Jamie Hayes
Abhradeep Thakurta
Adam Smith
Andreas Terzis
28
7
0
08 Oct 2024
It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With
  Non-Convex Loss
It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With Non-Convex Loss
Meenatchi Sundaram Muthu Selva Annamalai
31
8
0
09 Jul 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
Not all noise is accounted equally: How differentially private learning
  benefits from large sampling rates
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates
Friedrich Dörmann
Osvald Frisk
L. Andersen
Christian Fischer Pedersen
FedML
54
25
0
12 Oct 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
152
349
0
25 Sep 2021
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Nicolas Papernot
Abhradeep Thakurta
Shuang Song
Steve Chien
Ulfar Erlingsson
AAML
139
178
0
28 Jul 2020
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