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Reward-based spatial crowdsourcing with differential privacy preservation

Version 2 2024-06-06, 03:12
Version 1 2017-11-06, 10:57
journal contribution
posted on 2024-06-06, 03:12 authored by P Xiong, L Zhang, T Zhu
In recent years, the popularity of mobile devices has transformed spatial crowdsourcing (SC) into a novel mode for performing complicated projects. Workers can perform tasks at specified locations in return for rewards offered by employers. Existing methods ensure the efficiency of their systems by submitting the workers’ exact locations to a centralised server for task assignment, which can lead to privacy violations. Thus, implementing crowsourcing applications while preserving the privacy of workers’ location is a key issue that needs to be tackled. We propose a reward-based SC method that achieves acceptable utility as measured by task assignment success rates, while efficiently preserving privacy. A differential privacy model ensures rigorous privacy guarantee, and Laplace noise is introduced to protect workers’ exact locations. We then present a reward allocation mechanism that adjusts each piece of the reward for a task using the distribution of the workers’ locations. Through experimental results, we demonstrate that this optimised-reward method is efficient for SC applications.

History

Journal

Enterprise Information Systems

Volume

11

Pagination

1500-1517

Location

Abingdon, Eng.

ISSN

1751-7575

eISSN

1751-7583

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2016, Informa UK

Issue

10 : Curbing Collusive Cyber-gossips for Business Brand Management

Publisher

Taylor & Francis