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Paper Abstract and Keywords
Presentation 2019-05-23 14:00
Generation of privacy-preserving images holding positional information for HOG feature extraction
Masaki Kitayama, Hitoshi Kiya (Tokyo Metro. Univ.) IT2019-1 EMM2019-1
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a generation method of images which have no visual information but hold the gradient direction information of the original images.
Then we propose an extraction method of Histogram-of-Oriented-Gradients (HOG) features from the images (privacy-preserved images), and apply the features to machine learning algorithms.
The proposed generation method of privacy-preserved images is an irreversible method without any encryption keys.
Thereby, unlike conventional reversible methods to protect visual information by using encryption keys,
the proposed method does not need to consider secure management and transfer of the encryption keys.
Moreover, the proposed method solves the huge culculation cost problem that conventional methods based on homomorphic encryption have.
In this paper, in addition to a novel generation method of privacy-preserved images,
an effective method of extracting HOG features from the images is also considered.
In an experiment, a face classification task is carried out under the use of support vector machine with the HOG features to confirm the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) encryption / privacy-preserving / machine learning / HOG features / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 48, EMM2019-1, pp. 1-6, May 2019.
Paper # EMM2019-1 
Date of Issue 2019-05-16 (IT, EMM) 
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)
Download PDF IT2019-1 EMM2019-1

Conference Information
Committee EMM IT  
Conference Date 2019-05-23 - 2019-05-24 
Place (in Japanese) (See Japanese page) 
Place (in English) Asahikawa International Conference Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information Security, Information Theory, Information Hiding, etc. 
Paper Information
Registration To EMM 
Conference Code 2019-05-EMM-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Generation of privacy-preserving images holding positional information for HOG feature extraction 
Sub Title (in English)  
Keyword(1) encryption  
Keyword(2) privacy-preserving  
Keyword(3) machine learning  
Keyword(4) HOG features  
1st Author's Name Masaki Kitayama  
1st Author's Affiliation Tokyo Metropolitan University (Tokyo Metro. Univ.)
2nd Author's Name Hitoshi Kiya  
2nd Author's Affiliation Tokyo Metropolitan University (Tokyo Metro. Univ.)
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Speaker Author-1 
Date Time 2019-05-23 14:00:00 
Presentation Time 25 minutes 
Registration for EMM 
Paper # IT2019-1, EMM2019-1 
Volume (vol) vol.119 
Number (no) no.47(IT), no.48(EMM) 
Page pp.1-6 
Date of Issue 2019-05-16 (IT, EMM) 

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