Task Allocation Under Geo-Indistinguishability via Group-Based Noise Addition

Locations are usually necessary for task allocation in spatial crowdsourcing, which may put individual privacy in jeopardy without proper protection. Although existing studies have well explored the problem of location privacy protection in task allocation under geo-indistinguishability, they potentially assume the workers could perform any tasks, which might not be practical in reality. Moreover, they usually adopt planar laplacian mechanism to achieve geo-indistinguishability, which will introduce excessive noise due to its randomness and boundlessness. To this end, we propose a task alloCAtioN approach via grOup-based noisE addition under Geo-I, referred to as CANOE. Its main idea is that each worker uploads the noisy distances between his true location and the obfuscated locations of his preferred tasks instead of uploading his obfuscated location. In particular, to alleviate the total noise when conducting grouping, we put forward an optimized global grouping with adaptive local adjustment method OGAL with convergence guarantee. To collect the noisy distances which are required for subsequent task allocation, we develop a utility-aware obfuscated distance collection method UODC with solid privacy and utility guarantees. We further theoretically analyze the privacy, utility and complexity guarantees of CANOE. Extensive analyses and experiments over two real-world datasets confirm the effectiveness of CANOE.