Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2021-07-16 10:55
A Study on Decentralized Machine Learning with Differential Privacy based on Input Perturbation
Masakazu Okamoto, Koya Sato, Keiichi Iwamura (Tokyo Univ. of Science) SR2021-34
Abstract (in Japanese) (See Japanese page) 
(in English) Distributed machine learning eliminates the need for users to disclose their data to the out of the terminal since training can be done locally. However, machine learning has also been pointed out to have the potential to leak training data such as Model Inversion attack, which may lead to privacy violation. In this paper, we propose a method for satisfying differential privacy in distributed machine learning using input perturbations. Differential privacy is a definition for the privacy protection level, which can be satisfied by adding noise to the statistics. This allows us to analyze a large amount of data while ensuring privacy in distributed learning among users.
Numerical simulations demonstrate the accuracy of the proposed and related methods.
The results show that the proposed method can learn with higher accuracy than the output perturbation-based learning.
Keyword (in Japanese) (See Japanese page) 
(in English) distributed machine learning / differential privacy / input perturbation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 104, SR2021-34, pp. 67-72, July 2021.
Paper # SR2021-34 
Date of Issue 2021-07-07 (SR) 
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 SR2021-34

Conference Information
Committee RCS SR NS SeMI RCC  
Conference Date 2021-07-14 - 2021-07-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Communication and Network Technology of the AI Age, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc 
Paper Information
Registration To SR 
Conference Code 2021-07-RCS-SR-NS-SeMI-RCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Decentralized Machine Learning with Differential Privacy based on Input Perturbation 
Sub Title (in English)  
Keyword(1) distributed machine learning  
Keyword(2) differential privacy  
Keyword(3) input perturbation  
Keyword(4)  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Masakazu Okamoto  
1st Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
2nd Author's Name Koya Sato  
2nd Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
3rd Author's Name Keiichi Iwamura  
3rd Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2021-07-16 10:55:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2021-34 
Volume (vol) vol.121 
Number (no) no.104 
Page pp.67-72 
#Pages 6 
Date of Issue 2021-07-07 (SR) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan