| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 27, 2014 |
| Latest Amendment Date: | August 27, 2014 |
| Award Number: | 1423165 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Marilyn McClure
mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2014 |
| End Date: | September 30, 2018 (Estimated) |
| Total Intended Award Amount: | $200,000.00 |
| Total Awarded Amount to Date: | $200,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1523 UNION RD RM 207 GAINESVILLE FL US 32611-1941 (352)392-3516 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1 University of Florida Gainesville FL US 32611-2002 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CSR-Computer Systems Research |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Mobile Health (mHealth), particularly mobile healthcare remote monitoring, has been the most dynamic mobile apps in revolutionizing healthcare industries and steadily improving people's quality of life. Unfortunately, due to the nature of sensitive health related data handled by mHealth monitoring services, privacy issues become the stumbling blocks to wide deployment for the improvement of people?s quality of life and must be addressed appropriately. 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 concerns for both customers and mHealth service providers.
This project has developed an architectural framework, called CAM: a cloud-assisted mHealth monitoring system, by either leveraging the cloud computational power or developing distributed computational solutions to outsourcing computational workload while preserving the privacy of involved parties in mHealth. At a high level, this research is to develop enabling technologies for the potentially wide adoption of mHealth monitoring services. In particular, a security framework is designed to preserve the privacy of users' health data and companies' monitoring programs while still allowing the cloud to correctly execute the programs in use 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. When large volumes of data are distributed at various locations, privacy-preserving distributed machine learning algorithms have been designed to extract useful information without exchanging large volume distributed data by leveraging distributed computing resources. Finally, although motivated by mHealth monitoring applications, the proposed security framework can be generalized to suit privacy-preserving outsourcing of other diagnostic programs and practical applications such as smart grid, public health, and intelligent transportation.
This project has supported multiple graduate students (including a couple of minority students) who potentially become major players in healthcare industries and telecommunications industries and hence has trained next generation national work force. Particularly, two of the graduate students working on this project, including one female student, have become assistant professors in electrical and computer engineering departments in research-oriented universities after their graduation, continuing to train future engineers. The research findings can help general public better understand privacy issues and take precautions whenever they use any health-related mobile devices, help promote the privacy awareness in our society, and stimulate students' interest in pursuing careers in healthcare industries.
Last Modified: 12/31/2018
Modified by: Yuguang Fang
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