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

Dynamic Private Task Assignment under Differential Privacy