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Paper Abstract and Keywords
Presentation 0000-00-00 00:00
Data processing methods for high-performance Privacy-Preserving Machine Learning
Tatsuhiro Yamatsuki (Osaka Univ.), Atsuko Miyaji (Osaka Univ./JAIST) IT2023-94 ISEC2023-93 WBS2023-82 RCC2023-76
Abstract (in Japanese) (See Japanese page) 
(in English) Currently, attempts are being made in various fields to solve problems by utilizing big data. However, privacy protection is essential for the collection of personal data. In this context, local differential privacy has attracted attention as an index that can mathematically guarantee privacy protection not only for data users but also for data collectors. Local differential privacy is a method to protect data by adding random noise when data providers send data to data collectors. Local differential privacy has been attempted to be applied to machine learning. However, local differential privacy is difficult to control the usefulness and privacy protection for multidimensional data. Therefore, to apply local differential privacy to machine learning, it is necessary to suppress noise by combining dimensionality reduction and data discretization. In this study, we applied local differential privacy to machine learning by processing the data according to the distribution of the data to enable learning with higher accuracy. In addition, by adjusting the intensity of noise according to the attributes of the machine learning dataset, learning can be performed even under strict privacy conditions.
Keyword (in Japanese) (See Japanese page) 
(in English) Privacy, / Data utility / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 424, ISEC2023-93, pp. 118-126, March 2024.
Paper # ISEC2023-93 
Date of Issue 2024-03-06 (IT, ISEC, WBS, RCC) 
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 IT2023-94 ISEC2023-93 WBS2023-82 RCC2023-76

Conference Information
Committee RCC ISEC IT WBS  
Conference Date 2024-03-13 - 2024-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka Univ. (Suita Campus) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) RCC, ISEC, IT, WBS 
Paper Information
Registration To ISEC 
Conference Code 2024-03-RCC-ISEC-IT-WBS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Data processing methods for high-performance Privacy-Preserving Machine Learning 
Sub Title (in English)  
Keyword(1) Privacy,  
Keyword(2) Data utility  
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1st Author's Name Tatsuhiro Yamatsuki  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Atsuko Miyaji  
2nd Author's Affiliation Osaka University/Japan Advanced Institute of Science and Technology (Osaka Univ./JAIST)
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Date Time 0000-00-00 00:00:00 
Presentation Time minutes 
Registration for ISEC 
Paper # IT2023-94, ISEC2023-93, WBS2023-82, RCC2023-76 
Volume (vol) vol.123 
Number (no) no.423(IT), no.424(ISEC), no.425(WBS), no.426(RCC) 
Page pp.118-126 
#Pages 9 
Date of Issue 2024-03-06 (IT, ISEC, WBS, RCC) 

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