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Paper Abstract and Keywords
Presentation 2026-06-18 10:20
Vulnerability Analysis of Labels and Feature-Protected Data Against MIA
Jin Ashida, Atsuko Miyaji (Osaka Univ.) IA2026-16 ICSS2026-16
Abstract (in Japanese) (See Japanese page) 
(in English) Membership Inference Attack (MIA) assess machine learning risks by inferring if specific data was used in training. The Likelihood Ratio Attack (LiRA), proposed in 2022, established a new evaluation standard due to its high success and low false positive rates. While DP-SGD is a proven defense, the MIA resistance of Local Differential Privacy (LDP) mechanisms—specifically label-only and data-only LDP—remains underexplored. Existing research primarily focuses on the trade-off between accuracy and perturbation strength. In this study, we experimentally analyze the MIA resistance of both LDP methods using image and tabular datasets, and discuss key perturbations necessary for improving MIA resistance.
Keyword (in Japanese) (See Japanese page) 
(in English) Membership Inference Attack / Differential Privacy / Machine Learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 74, ICSS2026-16, pp. 100-107, June 2026.
Paper # ICSS2026-16 
Date of Issue 2026-06-10 (IA, ICSS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IA2026-16 ICSS2026-16

Conference Information
Committee IA ICSS  
Conference Date 2026-06-17 - 2026-06-18 
Place (in Japanese) (See Japanese page) 
Place (in English) e-Topia, Kagawa BB Square 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet Security, etc. 
Paper Information
Registration To ICSS 
Conference Code 2026-06-IA-ICSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Vulnerability Analysis of Labels and Feature-Protected Data Against MIA 
Sub Title (in English)  
Keyword(1) Membership Inference Attack  
Keyword(2) Differential Privacy  
Keyword(3) Machine Learning  
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1st Author's Name Jin Ashida  
1st Author's Affiliation The University of Osaka (Osaka Univ.)
2nd Author's Name Atsuko Miyaji  
2nd Author's Affiliation The University of Osaka (Osaka Univ.)
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Speaker Author-1 
Date Time 2026-06-18 10:20:00 
Presentation Time 25 minutes 
Registration for ICSS 
Paper # IA2026-16, ICSS2026-16 
Volume (vol) vol.126 
Number (no) no.73(IA), no.74(ICSS) 
Page pp.100-107 
#Pages 8 
Date of Issue 2026-06-10 (IA, ICSS) 

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