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Label-Only Membership Inference Attacks

Label-Only Membership Inference Attacks

28 July 2020
Christopher A. Choquette-Choo
Florian Tramèr
Nicholas Carlini
Nicolas Papernot
    MIACV
    MIALM
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Papers citing "Label-Only Membership Inference Attacks"

15 / 115 papers shown
Title
Survey: Leakage and Privacy at Inference Time
Survey: Leakage and Privacy at Inference Time
Marija Jegorova
Chaitanya Kaul
Charlie Mayor
Alison Q. OÑeil
Alexander Weir
Roderick Murray-Smith
Sotirios A. Tsaftaris
PILM
MIACV
23
71
0
04 Jul 2021
Membership Inference on Word Embedding and Beyond
Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar
Huseyin A. Inan
Melissa Chase
Esha Ghosh
Marcello Hasegawa
MIACV
SILM
25
46
0
21 Jun 2021
Bad Characters: Imperceptible NLP Attacks
Bad Characters: Imperceptible NLP Attacks
Nicholas Boucher
Ilia Shumailov
Ross J. Anderson
Nicolas Papernot
AAML
SILM
41
103
0
18 Jun 2021
Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference
  Perspective
Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective
Shahbaz Rezaei
Zubair Shafiq
Xin Liu
FedML
MIACV
40
13
0
12 May 2021
Membership Inference Attacks on Deep Regression Models for Neuroimaging
Membership Inference Attacks on Deep Regression Models for Neuroimaging
Umang Gupta
Dmitris Stripelis
Pradeep Lam
Paul M. Thompson
J. Ambite
Greg Ver Steeg
MIACV
FedML
29
32
0
06 May 2021
A Review of Confidentiality Threats Against Embedded Neural Network
  Models
A Review of Confidentiality Threats Against Embedded Neural Network Models
Raphael Joud
Pierre-Alain Moëllic
Rémi Bernhard
J. Rigaud
28
6
0
04 May 2021
Membership Inference Attacks on Machine Learning: A Survey
Membership Inference Attacks on Machine Learning: A Survey
Hongsheng Hu
Z. Salcic
Lichao Sun
Gillian Dobbie
Philip S. Yu
Xuyun Zhang
MIACV
35
412
0
14 Mar 2021
Membership Inference Attacks are Easier on Difficult Problems
Membership Inference Attacks are Easier on Difficult Problems
Avital Shafran
Shmuel Peleg
Yedid Hoshen
MIACV
19
16
0
15 Feb 2021
Quantifying and Mitigating Privacy Risks of Contrastive Learning
Quantifying and Mitigating Privacy Risks of Contrastive Learning
Xinlei He
Yang Zhang
21
51
0
08 Feb 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine
  Learning
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
Milad Nasr
Shuang Song
Abhradeep Thakurta
Nicolas Papernot
Nicholas Carlini
MIACV
FedML
82
216
0
11 Jan 2021
Membership Leakage in Label-Only Exposures
Membership Leakage in Label-Only Exposures
Zheng Li
Yang Zhang
34
237
0
30 Jul 2020
A Survey of Privacy Attacks in Machine Learning
A Survey of Privacy Attacks in Machine Learning
M. Rigaki
Sebastian Garcia
PILM
AAML
39
213
0
15 Jul 2020
Sponge Examples: Energy-Latency Attacks on Neural Networks
Sponge Examples: Energy-Latency Attacks on Neural Networks
Ilia Shumailov
Yiren Zhao
Daniel Bates
Nicolas Papernot
Robert D. Mullins
Ross J. Anderson
SILM
19
127
0
05 Jun 2020
Revisiting Membership Inference Under Realistic Assumptions
Revisiting Membership Inference Under Realistic Assumptions
Bargav Jayaraman
Lingxiao Wang
Katherine Knipmeyer
Quanquan Gu
David Evans
24
147
0
21 May 2020
A New Defense Against Adversarial Images: Turning a Weakness into a
  Strength
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Tao Yu
Shengyuan Hu
Chuan Guo
Wei-Lun Chao
Kilian Q. Weinberger
AAML
58
101
0
16 Oct 2019
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