Toward Selective Membership Inference Attack against Deep Learning Model-Reference-Cited by-同舟云学术

Toward Selective Membership Inference Attack against Deep Learning Model

Published:2022-11-01 Issue:11 Volume:E105.D Page:1911-1915
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

KWON Hyun¹,KIM Yongchul²

Affiliation:

1. Department of Artificial Intelligence and Data Science, Korea Military Academy

2. Department of Electrical Engineering, Korea Military Academy

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E105.D/11/E105.D_2022NGL0001/_pdf

Reference12 articles.

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2. [2] N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J.V. Pearson, D.A. Stephan, S.F. Nelson, and D.W. Craig, “Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays,” PLoS genetics, vol.4, no.8, 2008. 10.1371/journal.pgen.1000167

3. [3] M. Backes, P. Berrang, M. Humbert, and P. Manoharan, “Membership privacy in microrna-based studies,” Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp.319-330, 2016. 10.1145/2976749.2978355

4. [4] I. Hagestedt, Y. Zhang, M. Humbert, P. Berrang, H. Tang, X. Wang, and M. Backes, “Mbeacon: Privacy-preserving beacons for dna methylation data,” NDSS, 2019. 10.14722/ndss.2019.23064

5. [5] A. Sundararaj and G. Kul, “Impact analysis of training data characteristics for phishing email classification,” Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol.12, pp.85-98, 2021.

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