期刊
2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020)
卷 -, 期 -, 页码 517-528出版社
IEEE COMPUTER SOC
DOI: 10.1109/ICDE48307.2020.00051
关键词
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资金
- National Science Foundation of China (NSFC) [61729201, U1811463, 61822201]
- Hong Kong RGC GRF Project [16209519]
- Science and Technology Planning Project of Guangdong Province, China [2015B010110006]
- Hong Kong ITC ITF Grants [ITS/044/18FX, ITS/470/18FX]
- Didi-HKUST Joint Research Lab Grant
- Microsoft Research Asia Collaborative Research Grant
- Wechat Research Grant
With spatial crowdsourcing applications such as Uber and Waze deeply penetrated into everyday life, there is a growing concern to protect user privacy in spatial crowdsourcing. Particularly, locations of workers and tasks should be properly processed via certain privacy mechanism before reporting to the untrusted spatial crowdsourcing server for task assigmnent. Privacy mechanisms typically permute the location information, which tends to make task assignment ineffective. Prior studies only provide guarantees on privacy protection without assuring the effectiveness of task assigmnent. In this paper, we investigate privacy protection for online task assigmnent with the objective of minimizing the total distance, an important task assignment formulation in spatial crowdsourcing. We design a novel privacy mechanism based on Hierarchically Well-Separated Trees (HSTs). We prove that the mechanism is epsilon-Geo-Indistinguishable and show that there is a task assignment algorithm with a competitive ratio of O(1/epsilon(4) log N log(2) k), where epsilon is the privacy budget, N is the number of predefined points on the HST, and k is the matching size. Extensive experiments on synthetic and real datasets show that online task assignment under our privacy mechanism is notably more effective in terms of total distance than under prior differentially private mechanisms.
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