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Paper Abstract and Keywords
Presentation 2026-03-04 15:50
Nullifying Data Reconstruction Attacks in Federated Learning via Per-element Secure Aggregation
Takumi Suimon, Yuki Koizumi, Junji Takemasa (UOsaka), Toru Hasegawa (Shimane Univ.) IN2025-73
Abstract (in Japanese) (See Japanese page) 
(in English) In federated learning, original data are susceptible to reconstruction from individual updates shared by participants.
Secure Aggregation (SecAgg) mitigates this risk by sharing only aggregated results with the server.
However, it has been demonstrated that an adversarial server can extract a target update from the aggregated results by sparsifying updates from other participants.
This vulnerability stems from the fact that SecAgg ensures threshold aggregation at the vector level rather than the element level.
This paper proposes element-wise SecAgg, which aims to neutralize such attacks by disclosing only those elements where the number of non-zero values meets or exceeds a specified threshold.
Keyword (in Japanese) (See Japanese page) 
(in English) federated learning / secure aggregation / data reconstruction attacks / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 386, IN2025-73, pp. 61-66, March 2026.
Paper # IN2025-73 
Date of Issue 2026-02-25 (IN) 
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 IN2025-73

Conference Information
Committee IN NS  
Conference Date 2026-03-04 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa-Ken Shichoson Jichi Kaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To IN 
Conference Code 2026-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Nullifying Data Reconstruction Attacks in Federated Learning via Per-element Secure Aggregation 
Sub Title (in English)  
Keyword(1) federated learning  
Keyword(2) secure aggregation  
Keyword(3) data reconstruction attacks  
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1st Author's Name Takumi Suimon  
1st Author's Affiliation The University of Osaka (UOsaka)
2nd Author's Name Yuki Koizumi  
2nd Author's Affiliation The University of Osaka (UOsaka)
3rd Author's Name Junji Takemasa  
3rd Author's Affiliation The University of Osaka (UOsaka)
4th Author's Name Toru Hasegawa  
4th Author's Affiliation Shimane University (Shimane Univ.)
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Speaker Author-1 
Date Time 2026-03-04 15:50:00 
Presentation Time 25 minutes 
Registration for IN 
Paper # IN2025-73 
Volume (vol) vol.125 
Number (no) no.386 
Page pp.61-66 
#Pages 6 
Date of Issue 2026-02-25 (IN) 

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