TAMT: Privacy-Preserving Task Assignment with Multi-Threshold Range Search for Spatial Crowdsourcing Applications

Spatial crowdsourcing is a distributed computing paradigm that utilizes the collective intelligence of workers to perform complex tasks. How to achieve privacy-preserving task assignment in spatial crowdsourcing applications has been a popular research area. However, most of the existing task assignment schemes may reveal private and sensitive information of tasks or workers. Few schemes can support task assignment based on different attributes simultaneously, such as spatial, interest, etc. To study the above themes, in this paper, we propose one privacy-preserving task assignment scheme with multi-threshold range search for spatial crowdsourcing applications (TAMT). Specifically, we first define Euclidean distance-based location search and Hamming distance-based interest search, which map the demands of the tasks and the interests of the workers into the binary vectors. Second, we deploy PKD-tree to index the task data leveraging the pivoting techniques and the triangular inequality of Euclidean distance, and propose an efficient multi-threshold range search algorithm based on matrix encryption and decomposition technology. Furthermore, based on DT-PKC, we introduce a ciphertext-based secure comparison protocol to support multi-threshold range search for spatial crowdsourcing applications. Finally, comprehensive security analysis proves that our proposed TAMT is privacy-preserving. Meanwhile, theoretical analysis and experimental evaluation demonstrate that TAMT is practical and efficient.