Data collection is indispensable for spatial crowdsourcing services, such as resource allocation, policymaking, and scientific explorations. However, privacy issues make it challenging for users to share their information unless receiving sufficient compensation. Differential Privacy (DP) is a promising mechanism to release helpful information while protecting individuals' privacy. However, most DP mechanisms only consider a fixed compensation for each user's privacy loss. In this paper, we design a task assignment scheme that allows workers to dynamically improve their utility with dynamic distance privacy leakage. Specifically, we propose two solutions to improve the total utility of task assignment results, namely Private Utility Conflict-Elimination (PUCE) approach and Private Game Theory (PGT) approach, respectively. We prove that PUCE achieves higher utility than the state-of-the-art works. We demonstrate the efficiency and effectiveness of our PUCE and PGT approaches on both real and synthetic data sets compared with the recent distance-based approach, Private Distance Conflict-Elimination (PDCE). PUCE is always better than PDCE slightly. PGT is 50% to 63% faster than PDCE and can improve 16% utility on average when worker range is large enough.

翻译：数据收集对于空间众包服务（如资源分配、政策制定和科学探索）至关重要。然而，隐私问题使得用户不愿分享个人信息，除非获得足够补偿。差分隐私（DP）是一种在保护个人隐私的同时释放有用信息的有效机制。但现有DP机制大多仅考虑对每位用户隐私损失给予固定补偿。本文设计了一种任务分配方案，允许工作者通过动态距离隐私泄露来动态提升其效用。具体而言，我们提出了两种解决方案以提高任务分配结果的总效用：私有效用冲突消除（PUCE）方法和私有博弈论（PGT）方法。我们证明，PUCE实现的效用高于现有最优方法。通过在实际数据集和合成数据集上的实验，我们展示了所提PUCE和PGT方法相比近期基于距离的方法——私有距离冲突消除（PDCE）——的高效性与有效性。PUCE始终略优于PDCE。当工作者范围足够大时，PGT比PDCE快50%至63%，且平均可提升16%的效用。