TY - JOUR
T1 - Privacy-Aware Task Allocation and Data Aggregation in Fog-Assisted Spatial Crowdsourcing
AU - Wu, Haiqin
AU - Wang, Liangmin
AU - Xue, Guoliang
N1 - Funding Information:
This work was supported in part by the National Science Foundation grants 1421685, 1461886, 1704092, and 1717197, the National Natural Science Foundation of China under Grant U1736216, Grant 61472001, and Grant 61702233, the National Key Research and Development Program Grant 2017YFB1400703, and the Graduate student scientific research innovation projects of jiangsu province Grant KYCX17_1810. The information reported here does not reflect the position or the policy of the funding agencies. This work was done while H. Wu was visiting Arizona State University under the support of Chinese Scholarship Council (No. 201708320241).
Publisher Copyright:
© 2013 IEEE.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Spatial crowdsourcing (SC) enables task owners (TOs) to outsource spatial-related tasks to a SC-server who engages mobile users in collecting sensing data at some specified locations with their mobile devices. Data aggregation, as a specific SC task, has drawn much attention in mining the potential value of the massive spatial crowdsensing data. However, the release of SC tasks and the execution of data aggregation may pose considerable threats to the privacy of TOs and mobile users, respectively. Besides, it is nontrivial for the SC-server to allocate numerous tasks efficiently and accurately to qualified mobile users, as the SC-server has no knowledge about the entire geographical user distribution. To tackle these issues, in this paper, we introduce a fog-assisted SC architecture, in which many fog nodes deployed in different regions can assist the SC-server to distribute tasks and aggregate data in a privacy-aware manner. Specifically, a privacy-aware task allocation and data aggregation scheme (PTAA) is proposed leveraging bilinear pairing and homomorphic encryption. PTAA supports representative aggregate statistics (e.g., sum, mean, variance, and minimum) with efficient data update while providing strong privacy protection. Security analysis shows that PTAA can achieve the desirable security goals. Extensive experiments also demonstrate its feasibility and efficiency.
AB - Spatial crowdsourcing (SC) enables task owners (TOs) to outsource spatial-related tasks to a SC-server who engages mobile users in collecting sensing data at some specified locations with their mobile devices. Data aggregation, as a specific SC task, has drawn much attention in mining the potential value of the massive spatial crowdsensing data. However, the release of SC tasks and the execution of data aggregation may pose considerable threats to the privacy of TOs and mobile users, respectively. Besides, it is nontrivial for the SC-server to allocate numerous tasks efficiently and accurately to qualified mobile users, as the SC-server has no knowledge about the entire geographical user distribution. To tackle these issues, in this paper, we introduce a fog-assisted SC architecture, in which many fog nodes deployed in different regions can assist the SC-server to distribute tasks and aggregate data in a privacy-aware manner. Specifically, a privacy-aware task allocation and data aggregation scheme (PTAA) is proposed leveraging bilinear pairing and homomorphic encryption. PTAA supports representative aggregate statistics (e.g., sum, mean, variance, and minimum) with efficient data update while providing strong privacy protection. Security analysis shows that PTAA can achieve the desirable security goals. Extensive experiments also demonstrate its feasibility and efficiency.
KW - Spatial crowdsourcing
KW - bilinear pairing
KW - data aggregation
KW - fog computing
KW - homomorphic encryption
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U2 - 10.1109/TNSE.2019.2892583
DO - 10.1109/TNSE.2019.2892583
M3 - Article
AN - SCOPUS:85060290995
SN - 2327-4697
VL - 7
SP - 589
EP - 602
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
IS - 1
M1 - 8611203
ER -