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Paper Abstract and Keywords
Presentation 2015-11-27 14:00
[Poster Presentation] Secure Approximation Guarantee for Private Empirical Risk Minimization with Homomorphic Encryption
Toshiyuki Takada, Hiroyuki Hanada (NIT), Jun Sakuma (Univ.Tsukuba), Ichiro takeuchi (NIT) IBISML2015-86
Abstract (in Japanese) (See Japanese page) 
(in English) Privacy concern has been increasingly important in many machine learning problems. In this paper, we study empirical risk minimization (ERM) problems under secure multi-party computation (MPC) frameworks where the training set is distributed among multiple parties and each party wants to keep their data private.
Main technical tools for MPC have been borrowed from cryptography literature.
Although existing cryptographic tools are already efficient for basic calculations
such as addition or multiplication, it is still hard to use them for evaluating nonlinear functions such as logarithmic or exponential functions. When we apply these tool to a class of ERM problems in which many nonlinear function evaluations are required, we can only obtain an approximate solution. In this paper, we present
a novel privacy preserving protocol called secure approximation guarantee (SAG)
protocol. A key advantage of SAG protocol is that, given an arbitrary approximate solution, it can provide a non-probabilistic assumption-free bound on the
approximation quality under secure computation framework. We demonstrate the
advantage of the SAG protocol by applying it to a class of ERM problems.
Keyword (in Japanese) (See Japanese page) 
(in English) Privacy Preserving Machine Learning / Homomorphic Encryption / Convex Optimization / / / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 323, IBISML2015-86, pp. 249-256, Nov. 2015.
Paper # IBISML2015-86 
Date of Issue 2015-11-19 (IBISML) 
ISSN Print edition: ISSN 0913-5685    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 IBISML2015-86

Conference Information
Committee IBISML  
Conference Date 2015-11-25 - 2015-11-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Epochal Tsukuba 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2015) 
Paper Information
Registration To IBISML 
Conference Code 2015-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Secure Approximation Guarantee for Private Empirical Risk Minimization with Homomorphic Encryption 
Sub Title (in English)  
Keyword(1) Privacy Preserving Machine Learning  
Keyword(2) Homomorphic Encryption  
Keyword(3) Convex Optimization  
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1st Author's Name Toshiyuki Takada  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Hiroyuki Hanada  
2nd Author's Affiliation Nagoya Institute of Technology (NIT)
3rd Author's Name Jun Sakuma  
3rd Author's Affiliation University of Tsukuba (Univ.Tsukuba)
4th Author's Name Ichiro takeuchi  
4th Author's Affiliation Nagoya Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2015-11-27 14:00:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2015-86 
Volume (vol) vol.115 
Number (no) no.323 
Page pp.249-256 
#Pages 8 
Date of Issue 2015-11-19 (IBISML) 

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