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A Randomized Approach for Tight Privacy Accounting
Neural Information Processing Systems (NeurIPS), 2023
17 April 2023
Jiachen T. Wang
Saeed Mahloujifar
Tong Wu
R. Jia
Prateek Mittal
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Papers citing
"A Randomized Approach for Tight Privacy Accounting"
7 / 7 papers shown
Balls-and-Bins Sampling for DP-SGD
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Lynn Chua
Badih Ghazi
Charlie Harrison
Ethan Leeman
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
Amer Sinha
Chiyuan Zhang
340
8
0
21 Dec 2024
Near Exact Privacy Amplification for Matrix Mechanisms
International Conference on Learning Representations (ICLR), 2024
Christopher A. Choquette-Choo
Arun Ganesh
Saminul Haque
Thomas Steinke
Abhradeep Thakurta
362
13
0
08 Oct 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
C. Lebeda
Matthew Regehr
Gautam Kamath
Thomas Steinke
381
13
0
27 May 2024
How Private are DP-SGD Implementations?
Lynn Chua
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
Amer Sinha
Chiyuan Zhang
397
21
0
26 Mar 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization
Xinyu Tang
Ashwinee Panda
Milad Nasr
Saeed Mahloujifar
Prateek Mittal
566
40
0
09 Jan 2024
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
Georgios Kaissis
Jamie Hayes
Alexander Ziller
Daniel Rueckert
AAML
222
18
0
08 Jul 2023
Tight Auditing of Differentially Private Machine Learning
Milad Nasr
Jamie Hayes
Thomas Steinke
Borja Balle
Florian Tramèr
Matthew Jagielski
Nicholas Carlini
Seth Neel
FedML
257
68
0
15 Feb 2023
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