NSF CRII: NeTS: Embracing Dynamic Spectrum Sharing without Privacy Concerns

Project Information

CRII: NeTS: Embracing Dynamic Spectrum Sharing without Privacy Concerns, link, (CNS-1850523), Oct. 1, 2019 – Sep. 30, 2023.

Synopsis

The goal of this proposal is to limit unintended exposure of privacy information and design privacy mechanisms in centralized dynamic spectrum sharing systems while enabling efficient spectrum sharing. We aim to assess the impact of location privacy countermeasures used by mobile users in crowdsourced spectrum sensing on radio environment map (REM) construction and develop a quality-assured REM construction method to mitigate the dilemma between REM accuracy and location privacy. Obfuscation mechanisms will also be designed to protect primary users’ operating time privacy while ensuring the spectrum efficiency in spectrum allocation.

Personnel

  • Ms. Rui Hu, PhD student (graduated)
  • Mr. Yu Zhang, PhD student
  • Erick Galicia, Undergraduate student (REU)
  • Mario Alday, Undergraduate student (REU)
  • Andy Hernandez, Undergraduate student (REU)

Publications

  • R Hu, Y Guo, and Y Gong, “Energy-Efficient Distributed Machine Learning at Wireless Edge with Device-to-Device Communication,” IEEE ICC, 2022
  • R Chen, L Li, M Ying, Y Gong, Y Guo, T Ohtsuki, and M Pan, “Constructing Mobile Crowdsourced COVID-19 Vulnerability Map with Geo-Indistinguishability” IEEE Internet of Things Journal, 2022
  • S Amin, L Li, Y Guo, M Pan and Y Gong, “Geo-Indistinguishability for Crowdsourced-Based Radio Environment Map Construction” IEEE GLOBECOM, Taipei, Taiwan, Dec. 7-11, 2020.
  • R Chen, L Li, J Chen, R Hou, Y Gong, Y Yuanxiong and M Pan, “COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing”, IEEE GLOBECOM, Taipei, Taiwan, Dec. 7-11, 2020.
  • Huang, Zonghao and Hu, Rui and Guo, Yuanxiong and Chan-Tin, Eric and Gong, Yanmin “DP-ADMM: ADMM-Based Distributed Learning With Differential Privacy” IEEE Transactions on Information Forensics and Security , v.15 , 2020 10.1109/TIFS.2019.2931068
  • Hu, Rui and Guo, Yuanxiong and Li, Hongning and Pei, Qingqi and Gong, Yanmin “Privacy-Preserving Personalized Federated Learning” ICC 2020 - 2020 IEEE International Conference on Communications (ICC) , 2020 10.1109/ICC40277.2020.9149207
  • Huang, Zonghao and Pan, Miao and Gong, Yanmin “Robust Truth Discovery against Data Poisoning in Mobile Crowdsensing” 2019 IEEE Global Communications Conference (GLOBECOM) , 2019 10.1109/GLOBECOM38437.2019.9013890
  • Ding, Jiahao and Errapotu, Sai Mounika and Zhang, Haijun and Gong, Yanmin and Pan, Miao and Han, Zhu “Stochastic ADMM Based Distributed Machine Learning with Differential Privacy” Lecture notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering , 2019 10.1007/978-3-030-37228-6_13