SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things

Li, C; Yang, L; Yu, S; Qin, W; Ma, J

SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things

Li, C Yang, L Yu, S

Qin, W Ma, J

Permalink

Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Information Sciences, 2022, 612, pp. 151-167
Issue Date:: 2022-10-01

Embargoed

	Filename	Description	Size
	SEMMI Multi-party security decision-making scheme for linear functions in the internet of medical things.pdf	Accepted version	2.63 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Embargoed
Open Access

This item is currently unavailable due to the publisher's embargo.

The embargo period expires on 1 Oct 2024

Full metadata record

Field	Value	Language
dc.contributor.author	Li, C
dc.contributor.author	Yang, L
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Qin, W
dc.contributor.author	Ma, J
dc.date.accessioned	2023-01-25T00:20:15Z
dc.date.available	2023-01-25T00:20:15Z
dc.date.issued	2022-10-01
dc.identifier.citation	Information Sciences, 2022, 612, pp. 151-167
dc.identifier.issn	0020-0255
dc.identifier.uri	http://hdl.handle.net/10453/165420
dc.description.abstract	In the Internet of Medical Things(IoMT), developing models using machine learning algorithms can detect and assist users effectively in identifying health issues. Due to the risk of private user information being leaked during the machine learning application process, the widespread application and development of IoMT applications are hampered. Encrypting data is a good way to protect user privacy. However, given the participants’ limited resources, processing and analyzing ciphertext data presents a significant challenge. As a result, this paper proposes a secure and efficient assisted decision-making scheme (SEMMI) that is appropriate for the IoMT applications. SEMMI performs a thorough analysis of each participant's resource constraints, divides the data circulation process into four stages, and constructs a data circulation and ciphertext calculation protocol. Data transmission security is ensured through the use of stream encryption and homomorphic encryption. Each participant sends the ciphertext to the cloud, and the cloud calculates the ciphertext data, effectively relieving each participant's computational load. The security of the final result is guaranteed by matching the result with pre-decryption. The scheme's security and efficiency are demonstrated experimentally. The results indicate that the accuracy loss of each data set under the ciphertext is no more than 3% at most and that the cloud performs most of the calculations for each participant. Finally, SEMMI is applied to the FedAvg algorithm, demonstrating the scheme's universality.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2022.08.102
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things
dc.type	Journal Article
utslib.citation.volume	612
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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	embargoed	*
utslib.copyright.embargo	2024-10-01T00:00:00+1000Z
dc.date.updated	2023-01-25T00:20:08Z
pubs.publication-status	Published
pubs.volume	612

Abstract:

In the Internet of Medical Things(IoMT), developing models using machine learning algorithms can detect and assist users effectively in identifying health issues. Due to the risk of private user information being leaked during the machine learning application process, the widespread application and development of IoMT applications are hampered. Encrypting data is a good way to protect user privacy. However, given the participants’ limited resources, processing and analyzing ciphertext data presents a significant challenge. As a result, this paper proposes a secure and efficient assisted decision-making scheme (SEMMI) that is appropriate for the IoMT applications. SEMMI performs a thorough analysis of each participant's resource constraints, divides the data circulation process into four stages, and constructs a data circulation and ciphertext calculation protocol. Data transmission security is ensured through the use of stream encryption and homomorphic encryption. Each participant sends the ciphertext to the cloud, and the cloud calculates the ciphertext data, effectively relieving each participant's computational load. The security of the final result is guaranteed by matching the result with pre-decryption. The scheme's security and efficiency are demonstrated experimentally. The results indicate that the accuracy loss of each data set under the ciphertext is no more than 3% at most and that the cloud performs most of the calculations for each participant. Finally, SEMMI is applied to the FedAvg algorithm, demonstrating the scheme's universality.

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

http://hdl.handle.net/10453/165420