SecureBP from Homomorphic Encryption

Liu, Q; Lu, X; Luo, F; Zhou, S; He, J; Wang, K

SecureBP from Homomorphic Encryption

Liu, Q Lu, X Luo, F Zhou, S He, J Wang, K

Permalink

Publisher:: Hindawi Limited
Publication Type:: Journal Article
Citation:: Security and Communication Networks, 2020, 2020, pp. 1-9
Issue Date:: 2020-06-12

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (1.28 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, Q
dc.contributor.author	Lu, X
dc.contributor.author	Luo, F
dc.contributor.author	Zhou, S
dc.contributor.author	He, J
dc.contributor.author	Wang, K
dc.date.accessioned	2021-06-17T03:37:37Z
dc.date.available	2021-06-17T03:37:37Z
dc.date.issued	2020-06-12
dc.identifier.citation	Security and Communication Networks, 2020, 2020, pp. 1-9
dc.identifier.issn	1939-0114
dc.identifier.issn	1939-0122
dc.identifier.uri	http://hdl.handle.net/10453/149584
dc.description.abstract	<jats:p>We present a secure backpropagation neural network training model (SecureBP), which allows a neural network to be trained while retaining the confidentiality of the training data, based on the homomorphic encryption scheme. We make two contributions. The first one is to introduce a method to find a more accurate and numerically stable polynomial approximation of functions in a certain interval. The second one is to find a strategy of refreshing ciphertext during training, which keeps the order of magnitude of noise at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mover accent="true"><mml:mi>O</mml:mi><mml:mo>˜</mml:mo></mml:mover><mml:mfenced open="(" close=")" separators="\|"><mml:mrow><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mn>33</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfenced></mml:math>.</jats:p>
dc.language	en
dc.publisher	Hindawi Limited
dc.relation.ispartof	Security and Communication Networks
dc.relation.isbasedon	10.1155/2020/5328059
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0802 Computation Theory and Mathematics, 0805 Distributed Computing, 0899 Other Information and Computing Sciences
dc.title	SecureBP from Homomorphic Encryption
dc.type	Journal Article
utslib.citation.volume	2020
utslib.for	0802 Computation Theory and Mathematics
utslib.for	0805 Distributed Computing
utslib.for	0899 Other Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-06-17T03:37:36Z
pubs.publication-status	Published
pubs.volume	2020

Abstract:

We present a secure backpropagation neural network training model (SecureBP), which allows a neural network to be trained while retaining the confidentiality of the training data, based on the homomorphic encryption scheme. We make two contributions. The first one is to introduce a method to find a more accurate and numerically stable polynomial approximation of functions in a certain interval. The second one is to find a strategy of refreshing ciphertext during training, which keeps the order of magnitude of noise at O˜e33.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/149584