期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 18, 页码 13991-14002出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3069462
关键词
Task analysis; Crowdsourcing; Privacy; Encryption; Servers; Internet of Things; Differential privacy; Distributed system; privacy preserving; spatial crowdsourcing; task assignment
资金
- National Natural Science Foundation of China [61701216]
- Shenzhen Science, Technology and Innovation Commission Basic Research Project [JCYJ20180507181527806]
- Guangdong Provincial Key Laboratory [2020B121201001]
- Guangdong Innovative and Entrepreneurial Research Team Program [2016ZT06G587]
- Shenzhen SciTech Fund [KYTDPT20181011104007]
The article presents an online framework for assigning tasks to workers in a fully distributed manner while protecting location privacy. The system uses homomorphic encryption to protect the location privacy of both workers and tasks, and proposes novel wait-and-decide and proportional-backoff mechanisms to increase the number of assigned tasks efficiently and in a privacy-preserving manner.
With the proliferation of human-carried mobile devices, spatial crowdsourcing has emerged as a transformative system, where requesters outsource their spatiotemporal tasks to a set of workers who are willing to perform the tasks at the specified locations. However, in order to make efficient assignments, the existing spatial crowdsourcing system usually requires workers and/or tasks to expose their locations, which raises a significant concern of compromising location privacy. In addition, traditional spatial crowdsourcing systems employ a centralized server to manage the information of workers and tasks. Such a centralized design does not scale to a large number of workers/tasks, making the server easily a bottleneck. In this article, we present an online framework for assigning tasks to workers without compromising the location privacy in a fully distributed manner. Our system protects the location privacy of both workers and tasks through homomorphic encryption. We further propose a novel wait-and-decide mechanism and a proportional-backoff mechanism to increase the number of assigned tasks. Extensive experiments on real-world data sets illustrate that our proposed system achieves a large number of task assignments in an efficient and privacy-preserving manner.
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