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Bounding data reconstruction attacks with the hypothesis testing
  interpretation of differential privacy

Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy

8 July 2023
Georgios Kaissis
Jamie Hayes
Alexander Ziller
Daniel Rueckert
    AAML
ArXivPDFHTML

Papers citing "Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy"

10 / 10 papers shown
Title
(ε,δ)(\varepsilon, δ)(ε,δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees
Juan Felipe Gomez
B. Kulynych
G. Kaissis
Jamie Hayes
Borja Balle
Antti Honkela
51
0
0
13 Mar 2025
Attack-Aware Noise Calibration for Differential Privacy
Attack-Aware Noise Calibration for Differential Privacy
B. Kulynych
Juan Felipe Gomez
G. Kaissis
Flavio du Pin Calmon
Carmela Troncoso
44
6
0
02 Jul 2024
Data Reconstruction: When You See It and When You Don't
Data Reconstruction: When You See It and When You Don't
Edith Cohen
Haim Kaplan
Yishay Mansour
Shay Moran
Kobbi Nissim
Uri Stemmer
Eliad Tsfadia
AAML
37
2
0
24 May 2024
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical
  Adversaries
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
Rachel Cummings
Shlomi Hod
Jayshree Sarathy
Marika Swanberg
28
2
0
02 May 2024
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
31
0
0
12 Mar 2024
Bounding Reconstruction Attack Success of Adversaries Without Data
  Priors
Bounding Reconstruction Attack Success of Adversaries Without Data Priors
Alexander Ziller
Anneliese Riess
Kristian Schwethelm
Tamara T. Mueller
Daniel Rueckert
Georgios Kaissis
MIACV
AAML
24
1
0
20 Feb 2024
Reconciling AI Performance and Data Reconstruction Resilience for
  Medical Imaging
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
Alexander Ziller
Tamara T. Mueller
Simon Stieger
Leonhard F. Feiner
Johannes Brandt
R. Braren
Daniel Rueckert
Georgios Kaissis
53
1
0
05 Dec 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI
  models in medical imaging
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Soroosh Tayebi Arasteh
Alexander Ziller
Christiane Kuhl
Marcus R. Makowski
S. Nebelung
R. Braren
Daniel Rueckert
Daniel Truhn
Georgios Kaissis
MedIm
21
17
0
03 Feb 2023
Bounding Training Data Reconstruction in Private (Deep) Learning
Bounding Training Data Reconstruction in Private (Deep) Learning
Chuan Guo
Brian Karrer
Kamalika Chaudhuri
L. V. D. van der Maaten
103
53
0
28 Jan 2022
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
267
1,808
0
14 Dec 2020
1