A Blockchain-Based Privacy Protection Model Under Quality Consideration in Spatial Crowdsourcing Platforms
Spatial crowdsourcing (SC) is gaining popularity owing to the expansion of mobile devices and internet utilization, enabling cost-effective location-based task completion. However, relying on performing and submitting tasks to specific locations can raise concerns about privacy and task quality, whi...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10804118/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Spatial crowdsourcing (SC) is gaining popularity owing to the expansion of mobile devices and internet utilization, enabling cost-effective location-based task completion. However, relying on performing and submitting tasks to specific locations can raise concerns about privacy and task quality, which impacts SC effectiveness. Therefore, protecting workers’ privacy and ensuring high-quality task performance are crucial for trust and satisfaction, consequently, promoting the SC system’s success. This paper proposes a novel efficient Privacy Protection Task Assignment (ePPTA) model that incorporates centralized and decentralized platforms. This innovative model combines the strengths of centralized efficiency and decentralized privacy, and introduces a unique mechanism that significantly enhances privacy protection and ensures data integrity. Furthermore, the model enhances task performance quality by integrating task and worker constraints to effectively manage the task assignment process. The model was evaluated using a real-world dataset (Gowalla and Yelp), comparing its results with the most related state-of-the-art approaches through comprehensive testing and measuring its performance based on determined metrics. The ePPTA model achieves high utility with the Gowalla dataset, while reaching reasonable results with the Yelp dataset. Furthermore, it demonstrated significantly lower latency in both datasets than state-of-the-art approaches. Additionally, the ePPTA model has theoretically demonstrated its capability to prevent task tracking, eavesdropping attacks, and reward-reneging threats from external entities, thereby enhancing SC privacy protection. The results of the performance evaluation confirmed the efficiency of the proposed model, which is highly effective in addressing the identified challenges. |
|---|---|
| ISSN: | 2169-3536 |