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That which we call private
v1v2 (latest)

That which we call private

8 August 2019
Ulfar Erlingsson
Ilya Mironov
A. Raghunathan
Shuang Song
ArXiv (abs)PDFHTML

Papers citing "That which we call private"

13 / 13 papers shown
Title
Near Exact Privacy Amplification for Matrix Mechanisms
Near Exact Privacy Amplification for Matrix Mechanisms
Christopher A. Choquette-Choo
Arun Ganesh
Saminul Haque
Thomas Steinke
Abhradeep Thakurta
143
10
0
08 Oct 2024
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference
  Privacy in Machine Learning
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
A. Salem
Giovanni Cherubin
David Evans
Boris Köpf
Andrew Paverd
Anshuman Suri
Shruti Tople
Santiago Zanella Béguelin
134
40
0
21 Dec 2022
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis
  Testing: A Lesson From Fano
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
Chuan Guo
Alexandre Sablayrolles
Maziar Sanjabi
FedML
62
17
0
24 Oct 2022
Bayesian Estimation of Differential Privacy
Bayesian Estimation of Differential Privacy
Santiago Zanella Béguelin
Lukas Wutschitz
Shruti Tople
A. Salem
Victor Rühle
Andrew Paverd
Mohammad Naseri
Boris Köpf
Daniel Jones
76
40
0
10 Jun 2022
Bounding Membership Inference
Bounding Membership Inference
Anvith Thudi
Ilia Shumailov
Franziska Boenisch
Nicolas Papernot
71
18
0
24 Feb 2022
Improved Differential Privacy for SGD via Optimal Private Linear
  Operators on Adaptive Streams
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
S. Denisov
H. B. McMahan
J. Rush
Adam D. Smith
Abhradeep Thakurta
FedML
101
66
0
16 Feb 2022
Enhanced Membership Inference Attacks against Machine Learning Models
Enhanced Membership Inference Attacks against Machine Learning Models
Jiayuan Ye
Aadyaa Maddi
S. K. Murakonda
Vincent Bindschaedler
Reza Shokri
MIALMMIACV
110
256
0
18 Nov 2021
LinkTeller: Recovering Private Edges from Graph Neural Networks via
  Influence Analysis
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis
Fan Wu
Yunhui Long
Ce Zhang
Yue Liu
AAML
89
100
0
14 Aug 2021
Antipodes of Label Differential Privacy: PATE and ALIBI
Antipodes of Label Differential Privacy: PATE and ALIBI
Mani Malek
Ilya Mironov
Karthik Prasad
I. Shilov
Florian Tramèr
71
66
0
07 Jun 2021
D3p -- A Python Package for Differentially-Private Probabilistic
  Programming
D3p -- A Python Package for Differentially-Private Probabilistic Programming
Lukas Prediger
Niki Loppi
Samuel Kaski
Antti Honkela
76
6
0
22 Mar 2021
On the Privacy Risks of Algorithmic Fairness
On the Privacy Risks of Algorithmic Fairness
Hong Chang
Reza Shokri
FaML
195
113
0
07 Nov 2020
Investigating Membership Inference Attacks under Data Dependencies
Investigating Membership Inference Attacks under Data Dependencies
Thomas Humphries
Simon Oya
Lindsey Tulloch
Matthew Rafuse
I. Goldberg
Urs Hengartner
Florian Kerschbaum
MIACVMIALM
100
36
0
23 Oct 2020
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical
  Evaluation
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation
Ulfar Erlingsson
Vitaly Feldman
Ilya Mironov
A. Raghunathan
Shuang Song
Kunal Talwar
Abhradeep Thakurta
72
84
0
10 Jan 2020
1