Project Summary

Although current healthcare systems actively collect medical data from patients in hospitals, numerous personal subjective data is commonly neglected in the analysis of environmental public health due to high-sensitivity of health-related data. As a result, there is a lack of real-time monitoring data, such as symptom reports from high-risk groups and severe environmental pollution, causing notoriously long latency for effective prevention of the spread of epidemic diseases. This project is to address the fundamental challenges on collecting and analyzing multi-scale data from multi-sources for environmental public health in a privacy-preserving manner. The developed technologies empower each individual in a community to proactively contribute real-time data of themselves and surroundings for the betterment of public health without compromising his/her privacy. In addition, this project also serves as a training ground for educating future decision-makers and workforce on privacy-preserving healthcare technologies.

This multidisciplinary research advances the state-of-the-art public health by combining multi-scale data collection and analysis. Specifically, the project redesigns current healthcare monitoring systems for both severe infectious diseases and long-term environment-related diseases and their exacerbation (e.g., air pollutant-induced pulmonary diseases, such as chronic obstructive pulmonary disease and lung cancer). By considering the high sensitivity and distributed manner of the data from patients and users, this project addresses the privacy preservation in two-fold: 1) completely redesign efficient collaborative classification schemes by applying novel metrics without leaking individual's privacy; and 2) introduce new architectures to perform crowdsourcing data analysis by using light-weighted and verifiable encryption schemes. This project also grounds the theoretical outcomes to actual crowdsensing systems and social networks for validation. Finally, a new methodology on public health prediction model is developed with practical systematic implementation in healthcare systems.

Personnel

Principal Investigators

• Yuguang Fang, University of Florida, Leading PI
  
• Linke Guo, Binghamton University, PI
  
• Guirong Wang, SUNY Upstate Medical University (Upstate), PI
  

Graduate Students (at UF)

• Xianhao Chen, University of Florida, Ph.D student (10/2019-05/2022), who will be an Assistant Professor with the Department of Electrical and Electronic Engineering at University of Hong Kong, Hong Kong, China.
• Yaodan Hu, University of Florida, Ph.D student (10/2017-8/2018), who will be an Assistant Professor with the Department of Electrical and Computer Engineering at Idaho State University, ID, USA.
• Jianqing Liu, University of Florida, Ph.D student (10/2017-8/2018), who was an Assistant Professor with ECE at University of Alabama at Huntsville and will join the Department of Computer Science, North Carolina State University, NC, USA.
• Kaichen Yang, University of Florida, Ph.D student (10/2017-8/2018), who will be an Assistant Professor with the Department of Electrical and Computer Engineering at Michigan Technological University, MI, USA.
• Lan Zhang, University of Florida, Ph.D student (8/2018-8/2020), who is an Assistant Professor with the Department of Electrical and Computer Engineering at Michigan Technological University, MI, USA.
• Guangyu Zhu, University of Florida, Ph.D student (9/2019-present)

Publications

Copyright Notice

Papers downloadable on this page are under copyright protection. Please read and conform to all applicable copyright laws. Most downloadable papers are in PDF format, which can be viewed by PDF reader freely available from Adobe. Your comments are always welcome, please drop me a line at fang@ece.ufl.edu

Papers in Refereed Journals or Magazines

1. X. Chen, G. Zhu, H. Ding, L. Zhang, H. Zhang and Y. Fang, "End-to-end service auction: A general double auction mechanism for edge computing services,'' Accepted for publications in {\em IEEE/ACM Transactions on Networking.
2. X. Chen, G. Zhu and Y. Fang, "Federated learning over multi-hop wireless networks with in-network aggregation,'' Accepted for publication in IEEE Transactions on Wireless Communications. DOI: 10.1109/TWC.2022.3168538.
3. J. Liu, C. Zhang, K. Xue and Y. Fang, "Privacy preservation in multi-cloud secure data fusion for infectious-disease analysis,'' Accepted for publication in IEEE Transactions on Mobile Computing. DOI: 10.1109/TMC.2022.3145745.
4. X. Chen, L. Zhang, Y. Pang, B. Lin and Y. Fang, "Timeliness-aware incentive mechanism for vehicular crowdsourcing in smart cities,'' Accepted for publication in IEEE Transactions on Mobile Computing.
5. S. Jiang, J. Liu, Y. Zhou and Y. Fang, "FVC-Dedup: a secure report deduplication scheme in a fog-assisted vehicular crowdsensing system,'' Accepted for publications in IEEE Transactions on Dependable and Secure Computing. DOI: 10.1109/TDSC.2021.3069944.
6. D. Wang, X. Chen, L. Zhang, {\bf Y. Fang} and C. Huang, "A blockchain based human-to-infrastructure contact tracing approach for COVID-19,'' Accepted for publication in IEEE Internet of Things Journal. DOI: 10.1109/JIOT.2021.3138971.
7. Y. Deng, X. Chen, G. Zhu, Y. Fang, Z. Chen and X. Deng, "Actions at the edge: Jointly optimizing the resources in multi-access edge computing,'' IEEE Wireless Communications, vol.29, no.2, pp.192-198, April 2022.
8. X. Chen, Y. Ding, G. Zhu, D. Wang and Y. Fang, "From resource auction to service auction: An auction paradigm shift in wireless networks,'' IEEE Wireless Communications, vol.29, no.2, pp.185-191, April 2022.
9. X. Yuan, X. Ma, L. Zhang, Y. Fang and D. Wu, "Beyond class-level privacy leakage: breaking record-level privacy in federated learning,'' IEEE Internet of Things Journal, vol.9, no.4, pp.2555-2565, February 2022.
10. B. Lorenzo, F. J. Gozalez-Castano, L. Guo, F. J. Gil-Castineira and Y. Fang, "Autonomous robustness control for fog reinforcement in dynamic wireless networks,'' IEEE/ACM Transactions on Networking, vol.29, no.6, pp.2522-2535, December 2021.
11. X. Chen, G. Zhu, L. Zhang, Y. Fang, L. Guo and X. Chen, "Age-stratified COVID-19 spread analysis and vaccination: A multitype random network approach," IEEE Transactions on Network Science and Engineering, vol.8, no.2, pp.1862-1872, April/June 2021.
12. H. Ding, X. Li, Y. Ma and Y. Fang, "Energy-efficient channel switching in cognitive radio networks: a reinforcement learning approach,'' IEEE Transactions on Vehicular Technology, vol.69, no.10, pp.12359-12362, October 2020.
13. S. Jiang, J. Liu, L. Wang, Y. Zhou and Y. Fang, "ESAC: an efficient and secure access control scheme in vehicular named data networking,'' IEEE Transactions on Vehicular Technology, vol.69, no.9, pp.10252-10263, September 2020.
14. J. Wang, Q. Gao, X. Ma, Y. Zhao and Y. Fang, "Learning to sense: deep learning for wireless sensing with less training efforts,'' IEEE Wireless Communications, vol.27, no.3, pp. 156-162, June 2020.
15. L. Zhang, L. Yan, B. Lin, H. Ding, Y. Fang and X. Fang, "Augmenting transmission environments for better communications: tunable reflector assisted mmWave WLANs," IEEE Transactions on Vehicular Technology, vol.69, no.7, pp.7416-7428, July 2020.
16. J. Liu, C. Zhang, B. Lorenzo, Y. Fang, and S. Chen, "DPavatar: differentially private location protection for incumbent users in cognitive radio networks," IEEE Transactions on Mobile Computing, vol.19, no.3, pp.552-565, March 2020.
17. Q. Jia, L. Guo, Y. Fang, and G. Wang, "Efficient privacy-preserving machine learning in hierarchical distributed systems," IEEE Transactions on Network Science and Engineering, vol.6, no.4, pp.599-612, October-December 2019.
18. P. Huang, L. Guo, M. Li and Y. Fang, "Practical privacy-preserving ECG-based authentication for IoT-based healthcare," IEEE Internet of Things Journal, vol.6, no.5, pp.2327-4662, October 2019.
19. X. Zhang, P. Huang, L. Guo, and Y. Fang, "Social-aware energy-efficient data offloading with strong stability," IEEE/ACM Transactions on Networking, vol. 27, no. 4, pp. 1515-1528, August 2019.
20. Q. Jia, L. Guo, Z. Jin and Y. Fang, "Preserving model privacy for machine learning in distributed systems," IEEE Transactions on Parallel and Distributed Systems, vol.29, no.8, pp.1808-1822, August 2018.
21. X. Zhang, L. Guo, M. Li and Y. Fang, "Motivating human-enabled mobile participation for data offloading," IEEE Transactions on Mobile Computing, vol.17, no.7, pp.1624-1637, July 2018.
22. J. Liu, C. Zhang, Y. Fang, and J. Sun, "EPIC: a differential privacy framework to defend smart homes against Internet traffic analysis," IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1206-1217, April 2018.

1. X. Li, F. Ahmed, L. Wei, C. Zhang and Y. Fang, "Protecting access privacy in Ethereum using differentially private information retrieval,'' IEEE Globecom, Taipei, Taiwan, December 7-11, 2020. (Virtual)
2. X. Chen, L. Zhang, B. Lin and Y. Fang, "Delay-aware incentive mechanism for crowdsourcing with vehicles in smart cities," IEEE Globecom , Waikoloa, HI, USA, December 9-13, 2019.
3. L. Zhang, X. Chen, Y. Fang, X. Huang and X. Fang, "Learning-based mmWave V2I environment augmentation through tunable reflectors," IEEE Globecom, Waikoloa, HI, USA, December 9-13, 2019.
4. L. Zhang, L. Yan, B. Lin, Y. Fang and X. Fang, "Tunable reflectors enabled environment augmentation for better mmWave WLANs," IEEE/CIC International Conference on Communications in China (ICCC), Changchun, Jilin, China, August 11-13, 2019.
5. J. Liu, Y. Hu, H. Yue, Y. Gong and Y. Fang, "A cloud-based secure and privacy-preserving clustering analysis of infectious disease,'' The Second IEEE Symposium on Privacy-Aware Computing (PAC'18), Washington DC, USA, September 26-28, 2018.
6. H. Zhou, Y. Niu, J. Liu, C. Zhang, L. Wei and Y. Fang, "A privacy-preserving networked hospitality service with the bitcoin blockchain,'' The 13th International Conference on Wireless Algorithms, Systems, and Applications (WASA), Tianjin, China, June 20-22, 2018. Also in em>Lecture Notes in Computer Science, vol.10874.
7. Y. Hu, X. Li, J. Liu, H. Ding, Y. Gong and Y. Fang, "Mitigating traffic analysis attack in smartphones with edge network assistance,'' IEEE International Conference on Communications (ICC) , Kansas City, Missouri, USA, May 20-24, 2018.

Outreach and Education Activities for Broader Impact

Course Development

Student Mentoring

Research Dissemination

Talks/Seminars

1. Y. Fang, "Vehicles as a Service (VaaS): How to Leverage Vehicles to Beef Up the Edge,'' Invited Talk, Zhejiang University Overseas Academicians Workshop, Zhejiang University, Hangzhou, China, March 9-13, 2022. (Held Virtually).
2. Y. Fang, "Vehicles as a Service (VaaS): How to Leverage Vehicles to Beef Up the Edge,'' Invited Talk, The 3rd International Workshop on Internet of Vehicles and Edge Computing, Shanghai Jiao Tong University, Shanghai, China, January 22-23, 2022. (Held Virtually).
3. Y. Fang, "Beef Up the Edge: Building a Service Network for Sensing, Communications, Computing, Storage and Intelligence at the Edge,'' Keynote, The 23rd IEEE International Conferences on High Performance Computing and Communications (HPCC), Hyper-Intelligence Congress 2021, December 20-22, Haikou, Hainan, China (Held online due to pandemic).
4. Y. Fang, "Network as a Utility: How to Leverage ICT Technology to Intelligentize Life Sustaining Ecosystems,'' Keynote, Strategy Workshop for 2030 ICT Technology, Department of Information Technology, Ministry of Economic Affairs, Taiwan, December 11-12, 2020. (Held online due to pandemic.)
5. Y. Fang, "The Convergence of Sensing, Communications, Computing, Intelligentization and Storage (SCCIS): A Holistic Design Approach,'' Keynote, EAI SGIoT 2020--The 4th EAI International Conference on Smart Grid and Internet of Things, TaiChung, Taiwan, December 5-6, 2020. (Held online due to pandemic.)
6. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, The First Information Communication Technologies Conference (ICTC 2020), Nanjing, China, May 29-31, 2020. (Held online due to pandemic.)
7. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, 2019 International Workshop on Advances in Information Coding and Wireless Communications (AICWC'2019), Chengdu, Sichuan, China, October 30-November 2, 2019.
8. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, International Workshop on Communications Technology over Space/Air/Land/Sea, Dalian Maritime University, Liaoning, China, October 29-30, 2019.
9. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, The Second Symposium on High-Confidence Internet of Things, Qingdao, Shandong, China, October 28-29, 2019.
10. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, 2019 International Conference on Identification, Information & Knowledge in the Internet of Things, Jinan, Shandong, China, October 25-27, 2019.
11. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, 2019 Summit on Network Computing and AI, Changsha, Hunan, China, October 20-22, 2019.
12. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, 2019 International Conference on Image Processing and Machine Vision, Weihai, Shandong, China, September 28-30, 2019.
13. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, China Computing Federation (CCF) ``Three-Networks-into-One'' New Network Technology Workshop, Zhangjiajie, Hunan, China, August 22-24, 2019.
14. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, International Workshop on Intelligent Network and Edge Computing, School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong, China, August 14-15, 2019.
15. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, China Institute of Communications (CIC) Blue Sea Forum for Global Scientists in conjunction with 2019 Workshop on Scientific Research Fronts, China Communications Magazine, Hangzhou, Zhejiang, China, August 4-5, 2019.
16. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, International Workshop on Edge Computing, Hohhot, Inner Mongolia, July 20-22, 2019.
17. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, First Symposium on Computer Vision, Image Processing and Intelligence (CVIPI), Qingdao, Shandong, China, July 13-14, 2019.
18. Y. Fang, "Vehicles as a Service (VaaS): The Convergence of Communications, Computing, Storage and Intelligence (CCSI),'' Keynote, Frontier Research on Communications Technologies, Dalian Maritime University, June 7, 2019.
19. Y. Fang, "Connected Vehicles Make Cities Smarter: Communications, Computing, Storage, and Intelligence,'' Keynote, Symposium on Control and Security, Zhejiang University, May 18, 2019. Y. Fang, "Connected Vehicles Make Cities Smarter: Communications, Computing, Storage, and Intelligence,'' IEEE VTS Distinguished Lecture, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, May 16, 2019.
20. Y. Fang, "Connected Vehicles Make Cities Smarter: Communications, Computing, Storage, and Intelligence,'' IEEE VTS Distinguished Lecture, Department of Computer Science, National Tsinghua University, Hsinchu, Taiwan, May 13, 2019. Y. Fang, "Connected Vehicles Make Cities Smarter: Communications, Computing, Storage, and Intelligence," Keynote Speaker, 2019 International Workshop on Edge Computing and Intelligence, Shandong University, Jinan, Shandong, China, March 8-9, 2019.
21. Y. Fang, "Connected Vehicles Make Cities Smarter: Communications, Computing, Storage, and Intelligence," Keynote Speaker, 2018 International Workshop on Advances in Information Coding and Wireless Communications (AICWC'2018), Chengdu, China, November 30-December 2, 2018.
22. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," Keynote Speaker, China Institute of Communications (CIC) Blue Sea Forum for Global Scientists in conjunction with 2018 Workshop on Scientific Research Fronts, China Communications magazine, Dali, China, August 2-4, 2018.
23. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," Keynote Speaker, 2018 Workshop on the Edge Computing and Internet of Intelligence, Central South University, Changsha, China, July 17, 2018.
24. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," Keynote Speaker, 2018 International Workshop on the Internet of Things, University of Electrical Science and Technology of China, Chengdu, China, July 13-14, 2018.
25. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, July 2, 2018.
26. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," School of Information and Computer Science, Shandong Normal University, Jinan, Shandong, China, June 21, 2018.
27. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," School of Computer Science and Technology, Xiangtan University, Xiangtan, Hunan, China, June 18, 2018.
28. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," Academy for Advanced Interdisciplinary Study, Peking University, Beijing, China, June 9, 2018.
29. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," School of Geophysics and Information Engineering, China University of Petroleum, Beijing, China, June 7, 2018.
30. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," Department of Computer Science, San Francisco State University, San Francisco, CA, May 14, 2018.
31. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," IEEE Distinguished Lecture, IEEE Vehicular Technology Society, Ryerson University, Toronto, Canada, May 4, 2018.
32. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," IEEE Distinguished Lecture, IEEE Vehicular Technology Society, Western University, London, Canada, May 2, 2018.
33. Y. Fang, "Beef Up the Edge: How to Build an Efficient and Robust IoT System," IEEE Distinguished Lecture, IEEE Vehicular Technology Society, University of Waterloo, Waterloo, Canada, May 1, 2018.