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Computing Differential Privacy Guarantees for Heterogeneous Compositions
  Using FFT
v1v2 (latest)

Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT

24 February 2021
A. Koskela
Antti Honkela
ArXiv (abs)PDFHTMLGithub (280★)

Papers citing "Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT"

18 / 18 papers shown
Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)
Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)
Wenxuan Bao
Vincent Bindschaedler
AAML
318
0
0
21 Aug 2025
PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors
PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors
Hilal Asi
Vitaly Feldman
Hannah Keller
G. Rothblum
Kunal Talwar
FedML
411
1
0
14 Mar 2025
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
Juan Felipe Gomez
B. Kulynych
G. Kaissis
Jamie Hayes
Jamie Hayes
Borja Balle
Antti Honkela
480
0
0
13 Mar 2025
Weights Shuffling for Improving DPSGD in Transformer-based Models
Weights Shuffling for Improving DPSGD in Transformer-based Models
Jungang Yang
Zhe Ji
Liyao Xiang
331
1
0
22 Jul 2024
Closed-Form Bounds for DP-SGD against Record-level Inference
Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin
Boris Köpf
Andrew Paverd
Shruti Tople
Lukas Wutschitz
Santiago Zanella Béguelin
303
2
0
22 Feb 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization
Private Fine-tuning of Large Language Models with Zeroth-order Optimization
Xinyu Tang
Ashwinee Panda
Milad Nasr
Saeed Mahloujifar
Prateek Mittal
624
45
0
09 Jan 2024
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to
  Data Valuation
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Jiachen T. Wang
Yuqing Zhu
Yu Wang
R. Jia
Prateek Mittal
TDI
418
24
0
30 Aug 2023
Privacy-Preserving In-Context Learning for Large Language Models
Privacy-Preserving In-Context Learning for Large Language ModelsInternational Conference on Learning Representations (ICLR), 2023
Tong Wu
Ashwinee Panda
Jiachen T. Wang
Prateek Mittal
431
57
0
02 May 2023
A Randomized Approach for Tight Privacy Accounting
A Randomized Approach for Tight Privacy AccountingNeural Information Processing Systems (NeurIPS), 2023
Jiachen T. Wang
Saeed Mahloujifar
Tong Wu
R. Jia
Prateek Mittal
417
12
0
17 Apr 2023
Composition of Differential Privacy & Privacy Amplification by
  Subsampling
Composition of Differential Privacy & Privacy Amplification by Subsampling
Thomas Steinke
538
70
0
02 Oct 2022
Individual Privacy Accounting with Gaussian Differential Privacy
Individual Privacy Accounting with Gaussian Differential PrivacyInternational Conference on Learning Representations (ICLR), 2022
A. Koskela
Marlon Tobaben
Antti Honkela
400
29
0
30 Sep 2022
The Saddle-Point Accountant for Differential Privacy
The Saddle-Point Accountant for Differential Privacy
Wael Alghamdi
S. Asoodeh
Flavio du Pin Calmon
Juan Felipe Gomez
O. Kosut
Lalitha Sankar
Fei Wei
315
8
0
20 Aug 2022
Faster Privacy Accounting via Evolving Discretization
Faster Privacy Accounting via Evolving DiscretizationInternational Conference on Machine Learning (ICML), 2022
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
340
19
0
10 Jul 2022
Connect the Dots: Tighter Discrete Approximations of Privacy Loss
  Distributions
Connect the Dots: Tighter Discrete Approximations of Privacy Loss DistributionsProceedings on Privacy Enhancing Technologies (PoPETs), 2022
Vadym Doroshenko
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
333
60
0
10 Jul 2022
Learning Numeric Optimal Differentially Private Truncated Additive
  Mechanisms
Learning Numeric Optimal Differentially Private Truncated Additive Mechanisms
David M. Sommer
Lukas Abfalterer
Sheila Zingg
Esfandiar Mohammadi
244
4
0
27 Jul 2021
Optimal Accounting of Differential Privacy via Characteristic Function
Optimal Accounting of Differential Privacy via Characteristic Function
Yuqing Zhu
Jinshuo Dong
Yu Wang
323
129
0
16 Jun 2021
Numerical Composition of Differential Privacy
Numerical Composition of Differential PrivacyNeural Information Processing Systems (NeurIPS), 2021
Sivakanth Gopi
Y. Lee
Lukas Wutschitz
484
222
0
05 Jun 2021
Tight Accounting in the Shuffle Model of Differential Privacy
Tight Accounting in the Shuffle Model of Differential Privacy
A. Koskela
Mikko A. Heikkilä
Antti Honkela
FedML
349
20
0
01 Jun 2021
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