Efficient Privacy-Preserving Multi-Location Task Allocation in Fog-Assisted Vehicular Crowdsourcing-Reference-Cited by-同舟云学术

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Efficient Privacy-Preserving Multi-Location Task Allocation in Fog-Assisted Vehicular Crowdsourcing

Published:2024-06-09 Issue: Volume: Page:5214-5219
ISSN:
Container-title:ICC 2024 - IEEE International Conference on Communications
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Author:

Yu Yantao¹,Guan Yunguo²,Xue Xiaoping¹,Ma Jingxiao¹,Zhang Ellen Z.³,Lu Rongxing³

Affiliation:

1. College of Electronic and Information Engineering, Tongji University,Shanghai,China,201804

2. School of Information Security and Applied Computing, Eastern Michigan University,Ypsilanti,MI,USA,48197

3. University of New Brunswick,Faculty of Computer Science,Frederiction,NB,Canada,E3B 5A3

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10622104/10622158/10623008.pdf?arnumber=10623008

Reference8 articles.

1. Achieving Privacy-Preserving Vehicle Selection for Effective Content Dissemination in Smart Cities

2. Efficient Privacy-Preserving Task Allocation With Secret Sharing for Vehicular Crowdsensing

3. FooDNet: Toward an Optimized Food Delivery Network Based on Spatial Crowdsourcing

4. Efficient and Privacy-Preserving Similarity Range Query over Encrypted Time Series Data

5. Achieving Privacy-Preserving Discrete Frchet Distance Range Queries

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