Title: A blockchain-based privacy protection model for a spatial crowdsourcing platform

Authors: Amal Albilali; Maysoon Abulkhair; Manal Bayousef

Addresses: Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia ' Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia ' Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

Abstract: Spatial crowdsourcing (SC) involves collecting geographic information from a crowd of people using mobile devices, raising critical privacy issues regarding participants' location data. In this article, we propose an efficient privacy protection task assignment (ePPTA) model as a novel method that combines centralised and decentralised platforms to achieve privacy protection for worker location, worker identity, and task location during the task assignment (TA) process. Through a centralised SC platform, we achieve privacy protection using an elliptic curve cryptography (ECC), ensuring low user computational and communication overheads. The task assignment process and its data integrity are managed via blockchain technology. We evaluate our model on a real dataset, comparing it with state-of-the-art methods. The ePPTA model demonstrates low user computational and communication overheads and theoretically prevents task-tracking and eavesdropping attacks from external entities. Performance evaluation results confirm that the proposed model's efficiency is reasonable, providing robust privacy protection for SC.

Keywords: crowdsourcing; privacy; location privacy; spatial crowdsourcing; SC; blockchain.

DOI: 10.1504/IJSNET.2025.150850

International Journal of Sensor Networks, 2025 Vol.49 No.4, pp.209 - 230

Received: 18 Jun 2024
Accepted: 16 May 2025
Published online: 24 Dec 2025 *

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