Research on Skill-Aware Task Assignment Algorithm under Local Differential Privacy

FANG Xianjin; ZHEN Yaru; ZHANG Pengfei; HUANG Shanshan

doi:10.11999/JEIT250425

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FANG Xianjin, ZHEN Yaru, ZHANG Pengfei, HUANG Shanshan. Research on Skill-Aware Task Assignment Algorithm under Local Differential Privacy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250425

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FANG Xianjin, ZHEN Yaru, ZHANG Pengfei, HUANG Shanshan. Research on Skill-Aware Task Assignment Algorithm under Local Differential Privacy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250425

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Research on Skill-Aware Task Assignment Algorithm under Local Differential Privacy

doi: 10.11999/JEIT250425 cstr: 32379.14.JEIT250425

1.
Department of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
2.
State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan 232001, China

Funds: The National Natural Science Foundation of China (61572034)

Received Date: 2025-05-15
Rev Recd Date: 2025-09-16

Available Online: 2025-09-23

Abstract

Abstract

Objective With the proliferation of mobile smart devices and wireless networks, Spatial Crowdsourcing (SC) has emerged as a new paradigm for collaborative task execution. By leveraging workers’ real-time locations, SC platforms dynamically assign tasks to distributed participants. However, continuous exposure of location data creates privacy risks, including trajectory inference and identity disclosure, which reduce worker participation and threaten system sustainability. Existing privacy-preserving methods either rely on trusted third parties or apply traditional differential privacy mechanisms. The former incurs high costs and security vulnerabilities, whereas the latter struggles to balance the trade-off between noise magnitude and data utility, often reducing task matching accuracy. To address these challenges, this study proposes a skill-aware task assignment algorithm under Local Differential Privacy (LDP) that simultaneously enhances location privacy protection and task assignment performance. The algorithm is particularly effective in settings characterized by uneven skill distributions and complex task requirements. Methods To protect workers’ location privacy, a Clip–Laplace (CLP) mechanism is applied to perturb real-time location data under Local Differential Privacy (LDP), ensuring bounded noise while maintaining data utility. To mitigate mismatches between heterogeneous task requirements and imbalanced worker skills, an entropy-based metric is used to evaluate skill diversity. When entropy falls below a predefined threshold, a secondary screening strategy rebalances the distribution by suppressing common skills and prioritizing rare ones. A skill-aware Pruning Greedy task assignment algorithm (PUGR) is then developed. PUGR iteratively selects the worker–task pair with the highest marginal contribution to maximize skill coverage under spatiotemporal and budget constraints. To improve computational efficiency, three pruning strategies are integrated: time–distance pruning, high-reward pruning, and budget-infeasibility pruning. Finally, comparative and ablation experiments on three real-world datasets assess the method using multiple metrics, including Loss of Quality of Service (LQS), Average Remaining Budget Rate (ARBR), and Task Completion Rate (TCR). Results and Discussions Experimental results show that the CLP mechanism consistently achieves lower LQS than the traditional Laplace mechanism (LP) across different privacy budgets, effectively reducing errors introduced by noise (Fig. 2). For skill diversity, the entropy-based metric combined with secondary screening nearly doubles the average entropy of candidate workers on the TKY and NYC datasets, demonstrating its effectiveness in balancing skill distribution. In task assignment, the proposed PUGR algorithm completes most worker–task matchings within four iterations, thereby reducing redundant computation and accelerating convergence (Fig. 3). Regarding budget utilization, the ARBR under CLP remains close to the No Privacy (NoPriv) baseline, indicating efficient resource allocation (Fig. 4, Table 2). For task completion, the method achieves a TCR of up to 90% in noise-free settings and consistently outperforms Greedy, OE-ELA, and TsPY under CLP (Fig. 5). Ablation studies further validate the contributions of secondary screening and pruning strategies to overall performance improvement. Conclusions This study addresses two central challenges in spatial crowdsourcing: protecting workers’ location privacy and improving skill-aware task assignment. A task assignment framework is proposed that integrates the CLP mechanism with a skill-aware strategy under the LDP model. The CLP mechanism provides strong privacy guarantees while preserving data utility by limiting noise magnitude. An entropy-based metric combined with secondary screening ensures balanced skill distribution, substantially enhancing skill coverage and task execution success in multi-skill scenarios. The PUGR algorithm incorporates skill contribution evaluation with multiple pruning constraints, thereby reducing the search space and improving computational efficiency. Experiments on real-world datasets demonstrate the method’s superiority in terms of LQS, ARBR, and TCR, confirming its robustness, scalability, and effectiveness in balancing privacy protection with assignment performance. Future work will explore dynamic pricing mechanisms based on skill scarcity and personalized, adaptive incentives to foster fairness, long-term worker engagement, and the sustainable development of spatial crowdsourcing platforms.
- Spatial crowdsourcing,
- Task assignment,
- Local Differential Privacy (LDP),
- Skill diversity,
- Greedy algorithm

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References(26)

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