|Table of Contents|

Research on Spatio-Temporal Data Privacy Preserving Sharing Swarm Learning(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年04期
Page:
1-10
Research Field:
空间数据智能研究
Publishing date:

Info

Title:
Research on Spatio-Temporal Data Privacy Preserving Sharing Swarm Learning
Author(s):
Kang Haiyan; Wu Siyuan
(Computer School,Beijing Information Science and Technology University,Beijing 100192,China)
Keywords:
data sharing; spatio-temporal big data; swarm learning; distributed learning
PACS:
TP181
DOI:
10.3969/j.issn.1001-4616.2024.04.001
Abstract:
Achieving the sharing and collaborative analysis of spatio-temporal data can explore the potential value of data and boost the development of geographic information industry,but the privacy leakage of private data inhibits the sharing of spatio-temporal data.In order to further promote the degree of spatio-temporal data sharing and optimize the sharing effect while taking into account the economic benefits and legitimate rights of the participants,this paper proposes a spatio-temporal data privacy preserving sharing swarm learning(STDPPS-SL)method. Firstly,a multi-participant spatio-temporal data sharing network based on swarm learning is proposed in order to protect the ownership of participant's data and enable the process of spatio-temporal data sharing without revealing the contents of the data,while keep the equal rights of participants. Secondly,a differential privacy stochastic gradient descent algorithm based on the t-distribution is proposed in order to prevent the confidentiality of the participant's private data from being destroyed due to the privacy leakage during the sharing process,and in order to avoid economic losses to the participants. Finally,a scoring system is designed to quantify the credibility of the participants,in order to ensure the credibility of the data sharing results. Theoretical analysis proves that the proposed method(STDPPS-SL)can protect the private data ownership of the participants by satisfying strict differential privacy. Comparative experiments on open datasets show that the proposed method(STDPPS-SL)is able to realize the spatio-temporal data sharing process with the protection of participant's privacy,and the method balances the security and usability.

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Last Update: 2024-12-15