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Paper Abstract and Keywords
Presentation 2024-02-29 10:45
Proposal of a Data Leakage Attack against a Vertical Federated Learning System based on Knowledge Distillation
Takumi Suimon, Yuki Koizumi, Junji Takemasa, Toru Hasegawa (Osaka Univ.) NS2023-187
Abstract (in Japanese) (See Japanese page) 
(in English) Vertical federated learning is a method for participants who have data with the same samples but different features to collaboratively train a machine learning model while keeping their data private. In traditional vertical federated learning, the samples to be inferred are limited, and all participants have to involve during inference phase. To overcome these limitations, Vertical Federated Knowledge Transfer (VFedTrans) has been proposed. In VFedTrans, participants can make inference locally while keeping their data private by using latent representation derived from federated singular value decomposition (FedSVD). This approach also makes the data leakage attacks against the traditional vertical federated learning invalid for VFedTrans. However, this work proposes an attack in which a semi-honest participant infers a linear relationship between latent representation and the original data with neural network and then reconstructs data of other participants. Furthermore, we use two datasets on healthcare and finance and evaluate our attack method.
Keyword (in Japanese) (See Japanese page) 
(in English) Vertical Federated Learning / Knowledge Distillation / Knowledge Transfer / Privacy Attack / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 397, NS2023-187, pp. 90-95, Feb. 2024.
Paper # NS2023-187 
Date of Issue 2024-02-22 (NS) 
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 NS2023-187

Conference Information
Committee NS IN  
Conference Date 2024-02-29 - 2024-03-01 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2024-02-NS-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Proposal of a Data Leakage Attack against a Vertical Federated Learning System based on Knowledge Distillation 
Sub Title (in English)  
Keyword(1) Vertical Federated Learning  
Keyword(2) Knowledge Distillation  
Keyword(3) Knowledge Transfer  
Keyword(4) Privacy Attack  
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1st Author's Name Takumi Suimon  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Yuki Koizumi  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Junji Takemasa  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Toru Hasegawa  
4th Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2024-02-29 10:45:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2023-187 
Volume (vol) vol.123 
Number (no) no.397 
Page pp.90-95 
#Pages 6 
Date of Issue 2024-02-22 (NS) 

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