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| 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 |
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| 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. |
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| 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 | |
| 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 | SIS2020-13 |
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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 |
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| 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 |
| #Pages | 6 |
| Date of Issue | 2020-09-24 (SIS) |
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