Achieving Efficient and Privacy-Preserving Location-Based Task Recommendation in Spatial Crowdsourcing

In spatial crowdsourcing, location-based task recommendation schemes are widely used to match appropriate workers in desired geographic areas with relevant tasks from data requesters. To ensure data confidentiality, various privacy-preserving location-based task recommendation schemes have been prop...

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Bibliographic Details
Published in:IEEE transactions on dependable and secure computing 2024-07, Vol.21 (4), p.4006-4023
Main Authors: Song, Fuyuan, Liang, Jinwen, Zhang, Chuan, Fu, Zhangjie, Qin, Zheng, Guo, Song
Format: Article
Language:English
Subjects:
Cloud computing
Complexity
Computational efficiency
Confidentiality
Cost analysis
Crowdsourcing
Cybersecurity
Encryption
location
Pattern analysis
Performance evaluation
Privacy
privacy-preserving
Queries
range query
Servers
spatial crowdsourcing
Spatial data
Spatial databases
Task analysis
Task recommendation
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Summary:In spatial crowdsourcing, location-based task recommendation schemes are widely used to match appropriate workers in desired geographic areas with relevant tasks from data requesters. To ensure data confidentiality, various privacy-preserving location-based task recommendation schemes have been proposed, as cloud servers behave semi-honestly. However, existing schemes reveal access patterns, and the dimension of the geographic query increases significantly when additional information beyond locations is used to filter appropriate workers. To address the above challenges, this article proposes two efficient and privacy-preserving location-based task recommendation (EPTR) schemes that support high-dimensional queries and access pattern privacy protection. First, we propose a basic EPTR scheme (EPTR-I) that utilizes randomizable matrix multiplication and public position intersection test (PPIT) to achieve linear search complexity and full access pattern privacy protection. Then, we explore the trade-off between efficiency and security and develop a tree-based EPTR scheme (EPTR-II) to achieve sub-linear search complexity. Security analysis demonstrates that both schemes protect the confidentiality of worker locations, requester queries, and query results and achieve different security properties on access pattern assurance. Extensive performance evaluation shows that both EPTR schemes are efficient in terms of computational cost, with EPTR-II being 10^{3}\times 103× faster than the state-of-the-art scheme in task recommendation.
ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2023.3342239