Task Allocation under Geo-Indistinguishability via Group-based Noise Addition

Pengfei Zhang

Abstract:

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 geoindistinguishability,
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 realworld
datasets confirm the effectiveness of CANOE.