Preserving Location Privacy and Accurate Task Allocation in Edge-assisted Mobile Crowdsensing
Author:
Affiliation:
1. University of Nebraska-Lincoln,Department of Electrical and Computer Engineering,USA
2. Utah State University,Department of Electrical and Computer Engineering,USA
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9771381/9771540/09771956.pdf?arnumber=9771956
Reference14 articles.
1. Towards Preserving Worker Location Privacy in Spatial Crowdsourcing
2. P2AE: Preserving Privacy, Accuracy, and Efficiency in Location-dependent Mobile Crowdsensing
3. Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
4. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
5. Near-Optimal Allocation Algorithms for Location-Dependent Tasks in Crowdsensing
Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Location Privacy Preservation of Geospatial Data Using Error Based Transformation;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
2. Bayesian Inference and Greedy Task Allocation for Edge Computing Systems with Uncertainty;ICC 2023 - IEEE International Conference on Communications;2023-05-28
3. Privacy-Preserving Task Allocation and Decentralized Dispute Protocol in Mobile Crowdsourcing;ICC 2023 - IEEE International Conference on Communications;2023-05-28
4. Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles;IEEE Open Journal of the Computer Society;2022
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