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Paper Abstract and Keywords
Presentation 2019-12-12 14:35
A Dimensionality Reduction Method with Random Sampling for Privacy-Preserving Machine Learning
Ayana Kawamura, Kenta Iida, Hitoshi Kiya (Tokyo Metro. Univ.) SIS2019-26
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a dimensionality reduction method with random sampling for privacy-preserving machine learning.Recently, cloud computing is spreading in many fields. However, the cloud computing has some serious issues for end users, such as unauthorized use and leak of data, and privacy compromise, due to unreliability of providers and some accidents.In addition, due to a huge amount of data, a dimensionality reduction technique is generally carried out in the case of applying image data to machine learning.Because of such a situation, we consider a machine learning scheme considering both privacy-preserving and dimensionality reduction.In this paper, we propose a novel dimensionality reduction technique that is carried out by dividing an image into blocks and sampling the blocks randomly.The proposed scheme allows us to preserve visual information of images and maintain the relative spatial relation between images.In addition, the proposed scheme has a feature that any secret-key management is not required.Some face recognition experiments are carried out by using a support vector machine algorithm as an example of machine learning algorithms to demonstrate the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) dimensionality reduction / machine learning / SVM / privacy-preserving / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 335, SIS2019-26, pp. 17-21, Dec. 2019.
Paper # SIS2019-26 
Date of Issue 2019-12-05 (SIS) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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)
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Conference Information
Committee SIS  
Conference Date 2019-12-12 - 2019-12-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Okayama University of Science 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Smart Personal Systems, etc. 
Paper Information
Registration To SIS 
Conference Code 2019-12-SIS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Dimensionality Reduction Method with Random Sampling for Privacy-Preserving Machine Learning 
Sub Title (in English)  
Keyword(1) dimensionality reduction  
Keyword(2) machine learning  
Keyword(3) SVM  
Keyword(4) privacy-preserving  
1st Author's Name Ayana Kawamura  
1st Author's Affiliation Tokyo Metropolitan University (Tokyo Metro. Univ.)
2nd Author's Name Kenta Iida  
2nd Author's Affiliation Tokyo Metropolitan University (Tokyo Metro. Univ.)
3rd Author's Name Hitoshi Kiya  
3rd Author's Affiliation Tokyo Metropolitan University (Tokyo Metro. Univ.)
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Speaker Author-1 
Date Time 2019-12-12 14:35:00 
Presentation Time 20 minutes 
Registration for SIS 
Paper # SIS2019-26 
Volume (vol) vol.119 
Number (no) no.335 
Page pp.17-21 
Date of Issue 2019-12-05 (SIS) 

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