College of Engineering

CAM: A Cloud-Assisted mHealth Monitoring System

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Project Information

  • CSR: Small: Collaborative Research: CAM: A Cloud-Assisted mHealth Monitoring System (CNS-1423165), PI. National Science Foundation, October 1, 2014-September 30, 2014. This is a collaborative project with Dr. Stella Sun, University of Tennessee at Knoxville. University of Florida is the leading institution.
  • This webpage and the materials provided here are based upon work supported by the National Science Foundation under Grant No. CNS-1423165.

    Project Summary

    Mobile Health (mHealth), particularly mobile healthcare monitoring, has been perceived to be the most dynamic mobile apps which play a crucial role in revolutionizing healthcare industries and steadily improving the quality of individuals' lives. Unfortunately, due to the sensitive and private nature of the health and fitness related data handled by mHealth monitoring services, privacy issues become the stumbling blocks to wide deployment and must be addressed. With limited capital investments, small to medium sized mHealth companies may have to seek cloud computing facilities to reduce the cost on IT support. However, outsourcing to the cloud will aggravate the privacy issues since companies' monitoring programs are also proprietary information. This project focuses on designing an architectural framework, called CAM: a cloud-assisted mHealth monitoring system, developing it into a middleware, and outsourcing expensive computations to the cloud. At a high level, the proposed research is to develop an enabling technology for the potentially wide adoption of mHealth monitoring services. In particular, a security framework is designed to preserve the privacy of users' health and fitness data and companies' monitoring programs while still allowing the cloud to correctly execute the programs and return proper advices to users. The design takes the outsourcing paradigm into account by shifting most computationally intensive tasks to the cloud while still preserving privacy, which is the key to producing a practically deployable system. The framework is then developed into a middleware by tackling practical issues such as a suitable programming model, balancing between security guarantees and flexibility for app developers, etc. Comprehensive penetration testing is conducted by simulating unique attacks to evaluate the security of the proposed framework in practical system settings. Although motivated by mHealth monitoring applications, the proposed security framework can be generalized for privacy-preserving outsourcing of diagnostic programs which have many other important applications such as financial analysis and software fault diagnosis. The proposed research will thus have broader impact by contributing to multiple disciplines and offering both graduate and undergraduate students plentiful opportunities for multidisciplinary research.

    Personnel at UF

    Personnel at Collaborative Institutions



    Papers in Refereed Journals

    1. Y. Yang, J. Sun and L. Guo, "PersonaIA: a lightweight implicit authentication system based on customized user behavior selection," under second round review for publication in IEEE Transactions on Dependable and Secure Computing
    2. Y. Tong, J. Sun, K. Sun and P. Li, "Secure outsourcing of power system dynamic simulation leveraging cloud computing," under second round review for publication in IEEE Transactions on Smart Grid (TSG) (Special Issue on High Performance Computing (HPC) Applications for a More Resilient and Efficient Power Grid)
    3. A. Thapa, W. Liao, M. Li, P. Li and J. Sun, "SPA: a secure and private auction framework for decentralized online social networks," Accepted for publication in IEEE Transactions on Parallel and Distributed Computing.
    4. Y. Gong, C. Zhang, Y. Fang and J. Sun, "Protecting location privacy for task allocation in ad hoc mobile cloud computing," Accepted for publication in IEEE Transactions on Emerging Topics in Computing.
    5. K. Xu, Y. Guo, L. Guo, Y. Fang and X. Li, "My privacy my decision: control of photo sharing on online social networks," Accepted for publication in IEEE Transactions on Dependable and Secure Computing.
    6. Y. Guo, L. Wei, Y. Guo, C. Zhang and Y. Fang, "Optimal task recommendation for mobile crowdsourcing with privacy control," IEEE Internet of Things Journal, vol.3, no.5, pp.745-756, October 2016.
    7. Y. Gong, Y. Fang and Y. Guo, "Private data analytics on biomedical sensing data via distributed computation,'' IEEE/ACM Transactions on Computational Biology and Bioinformatics (Special issue on Emerging Security Trends), vol.13, no.3, pp.431-444, May/June 2016.
    8. Y. Gong, Y. Cai, Y. Guo and Y. Fang, "A privacy-preserving scheme for incentive-based demand response in the smart grid,'' IEEE Transactions on Smart Grid, vol.7, no.3, pp.1304-1313, May 2016.
    9. L. Guo, C. Zhang, Y. Fang and P. Lin, "A privacy-preserving attribute-based reputation system in online social networks," Journal of Computer Science and Technology, vol.30, no.3, pp.578-597, May 2015.
    10. H. Zhao, M. Pan, X. Liu, X. Li and Y. Fang,"Exploring fine-grained resource rental planning in cloud computing," IEEE Transactions on Cloud Computing (Special Issue on Cloud Economics), vol.3, no.3, pp.304-317, July-September 2015.
    11. M. Li, S. Salinas, P. Li, J. Sun, and X. Huang, "MAC-layer selfish misbehavior in ieee 802.11 ad hoc networks: Detection and defense," IEEE Transactions on Mobile Computing, vol. 14, no. 6, pp.1203-1217, 2015.

    Papers in Refereed Conferences

    1. Y. Gong, C. Zhang, Y. Hu and Y. Fang, "Privacy-preserving genome-aware remote health monitoring,'' IEEE Global Communications Conference (Globecom), Washington, DC, December 4-8, 2016.
    2. T. Zhang, S.S. Chow and J. Sun, "Password-controlled encryption with accountable break-glass access," The 11th ACM on Asia Conference on Computer and Communications Security (AsiaCCS), pp. 235-246, ACM.
    3. Q. Jia, L. Guo, Z. Jin and Y. Fang, "Privacy-preserving data classification and similarity evaluation for distributed systems," The 36th International Conference on Distributed Computing Systems (IEEE ICDCS'16), Nara, Japan, Jun. 27-Jun. 30, 2016.
    4. G. Zhuo, Q. Jia, L. Guo, M. Li and Y. Fang, "Privacy-preserving verifiable proximity test for location-based services," IEEE Globecom, San Diego, California, 6-10 December, 2015. Won the Best Paper Award.
    5. Y. Gong, Y. Fang and Y. Guo, "Privacy-preserving collaborative learning for mobile health monitoring," IEEE Globecom, San Diego, California, 6-10 December, 2015.
    6. Y. Tong, J. Sun, K. Sun, and P. Li, "Outsourcing power system simulations," IEEE Globecom, San Diego, California, 6-10 December, 2015.
    7. Y. Tong, J. Sun, K. Sun, and P. Li, "Privacy-preserving spectral estimation in smart grid," Proceedings of the 6th IEEE International Conference on Smart Grid Communications (SmartGridCom'2015), 2015.
    8. Y. Yang, J. Sun, C. Zhang, and P. Li, "Model retraining and dynamic privilege-based access control for implicit authentication systems," Proceedings of the 12th IEEE International Conference on Mobile Ad hoc and Sensor Systems (MASS'2015), 2015.
    9. K. Xu, H. Yue, L. Guo, Y. Guo and Y. Fang, "Privacy-preserving machine learning algorithms for big data systems,'' The 35th IEEE International Conference on Distributed Computing Systems (ICDCS'2015), Columbus, Ohio, 29 June-2 July, 2015.
    10. X. Niu, J. Sun, and H. Li, "Network steganography based on traffic behavior in wireless sensor networks," IEEE ICC, London, UK, June 2015.
    11. L. Guo, Y. Fang, M. Li and P Li, "Verifiable privacy-preserving monitoring for cloud-assisted mHealth systems,'' The 34nd Annual IEEE International Conference on Computer Communications (INFOCOM'15), Hong Kong, 26 April-1 May, 2015.
    12. Y. Gong, Y. Guo and Y. Fang, "A privacy-preserving task recommendation framework for mobile crowdsourcing,'' IEEE Globecom, Austin, Texas, 8-12 December, 2014.
    13. M. Yu, K. Yang, L. Wei, C. Zhang, and J. Sun, "Practical private information retrieval supporting keyword search in the cloud," 2014 International Conference on Wireless Communications and Signal Processing, Hefei, Anhui, China, October 2014.

    PhD Dissertations

    1. Y. Gong, Privacy-Preserving Data Analytics for Big Data Applications, PhD Dissertation, University of Florida, August 2016.
    2. K. Xu, Secure Collaborative Machine Learning in Big Data Systems, PhD Dissertation, University of Florida, August 2015.
    3. Y. Tong, Data Security and Privacy in Smart Grids, PhD Dissertation, University of Tennessee, 2015.

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