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Paper Abstract and Keywords
Presentation 2025-06-05 15:25
Application of the Membership Inference Attacks Using Poisoning to Federated Learning
Tomoya Tsukada, Masahiro Mambo (Kanazawa Univ.) SIP2025-13 BioX2025-13 IE2025-13 MI2025-13
Abstract (in Japanese) (See Japanese page) 
(in English) Even though it is known that membership inference attack becomes more effective against deep learning by using data poisoning, not so much results have been reported so far about the effect of such an attack against federated learning. In this paper, we conduct experiments on the application of the membership inference attacks using poisoned data to federated learning and show that the attack success rate of membership inference attacks can be improved by utilizing poisoning in federated learning. Furthermore, we show that the proportion of poisoned data and the number of clients using poisoned data affect the success rate of the membership inference attack against federated learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Federated learning / Membership Inference Attack / Poisoning Attack / Deep Learning / Privacy Preservation / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 54, BioX2025-13, pp. 71-76, June 2025.
Paper # BioX2025-13 
Date of Issue 2025-05-29 (SIP, BioX, IE, MI) 
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 SIP2025-13 BioX2025-13 IE2025-13 MI2025-13

Conference Information
Committee ITE-IST ITE-ME IE BioX SIP MI  
Conference Date 2025-06-05 - 2025-06-06 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To BioX 
Conference Code 2025-06-IST-ME-IE-BioX-SIP-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Application of the Membership Inference Attacks Using Poisoning to Federated Learning 
Sub Title (in English)  
Keyword(1) Federated learning  
Keyword(2) Membership Inference Attack  
Keyword(3) Poisoning Attack  
Keyword(4) Deep Learning  
Keyword(5) Privacy Preservation  
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1st Author's Name Tomoya Tsukada  
1st Author's Affiliation Kanazawa University (Kanazawa Univ.)
2nd Author's Name Masahiro Mambo  
2nd Author's Affiliation Kanazawa University (Kanazawa Univ.)
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Speaker Author-1 
Date Time 2025-06-05 15:25:00 
Presentation Time 25 minutes 
Registration for BioX 
Paper # SIP2025-13, BioX2025-13, IE2025-13, MI2025-13 
Volume (vol) vol.125 
Number (no) no.53(SIP), no.54(BioX), no.55(IE), no.56(MI) 
Page pp.71-76 
#Pages 6 
Date of Issue 2025-05-29 (SIP, BioX, IE, MI) 

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