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Paper Abstract and Keywords
Presentation 2020-10-01 13:00
Evaluation of linear dimensionality reduction methods considering visual information protection for privacy-preserving machine learning
Masaki Kitayama, Nobutaka Ono, Hitoshi Kiya (Tokyo Metro. Univ.) SIS2020-13
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, linear dimensionality reduction methods are evaluated in terms of difficulty in estimating the visual information of original images from dimensionally reduced ones.
Dimensionality reduction in machine learning has been widely used to avoid negative effects that high-dimensional data have on machine learning models.
In recent years, dimensionality reduction methods are also used for protecting the visual information of images for privacy-preserving machine learning.
In this paper, we apply typical linear dimensionality reduction methods to image data, and experimentally evaluate their robustness against various possible visual information estimation attacks.
Keyword (in Japanese) (See Japanese page) 
(in English) dimensionality reduction / machine learning / privacy-preserving / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 176, SIS2020-13, pp. 17-22, Oct. 2020.
Paper # SIS2020-13 
Date of Issue 2020-09-24 (SIS) 
ISSN 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 ITE-BCT  
Conference Date 2020-10-01 - 2020-10-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) System Implementation Technology, Short Range Wireless Systems, Smart Multimedia Systems, Broadcasting Technology, etc. 
Paper Information
Registration To SIS 
Conference Code 2020-10-SIS-BCT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of linear dimensionality reduction methods considering visual information protection for privacy-preserving machine learning 
Sub Title (in English)  
Keyword(1) dimensionality reduction  
Keyword(2) machine learning  
Keyword(3) privacy-preserving  
1st Author's Name Masaki Kitayama  
1st Author's Affiliation Tokyo Metropolitan University (Tokyo Metro. Univ.)
2nd Author's Name Nobutaka Ono  
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 2020-10-01 13:00:00 
Presentation Time 20 minutes 
Registration for SIS 
Paper # SIS2020-13 
Volume (vol) vol.120 
Number (no) no.176 
Page pp.17-22 
Date of Issue 2020-09-24 (SIS) 

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