PPFO: A Privacy Preservation-oriented Data Freshness Optimization Framework For Mobile Crowdsensing
Author:
Affiliation:
1. University of PLA,Army Engineering,China
2. City University of Hong Kong,China
3. Henan Polytechnic University,China
4. Chongqing University,China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Management of Technology and Innovation,Law,Computer Networks and Communications,Safety, Risk, Reliability and Quality
Link
http://xplorestaging.ieee.org/ielx7/7886829/10352978/10352980.pdf?arnumber=10352980
Reference15 articles.
1. A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
2. Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel Attacks
3. Mobile Crowdsensing for Data Freshness: A Deep Reinforcement Learning Approach
4. BSIF: Blockchain-Based Secure, Interactive, and Fair Mobile Crowdsensing
5. Edge-Assisted Public Key Homomorphic Encryption for Preserving Privacy in Mobile Crowdsensing
Cited by 2 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. Achieving lightweight, efficient, privacy-preserving user recruitment in mobile crowdsensing;Journal of Information Security and Applications;2024-09
2. Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing;IEEE Wireless Communications;2024
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