
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | August 17, 2017 |
Latest Amendment Date: | August 17, 2017 |
Award Number: | 1718078 |
Award Instrument: | Standard Grant |
Program Manager: |
Dan Cosley
dcosley@nsf.gov (703)292-8832 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | September 1, 2017 |
End Date: | August 31, 2021 (Estimated) |
Total Intended Award Amount: | $500,000.00 |
Total Awarded Amount to Date: | $500,000.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
550 S COLLEGE AVE NEWARK DE US 19713-1324 (302)831-2136 |
Sponsor Congressional District: |
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Primary Place of Performance: |
18 Amstel Ave, 448 Smith Hall Newark DE US 19716-2599 |
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): | Secure &Trustworthy Cyberspace |
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 Cloud Sensing (MCS) is a promising paradigm for collaborative information collection and sharing in emerging smart cities. MCS is based on the fundamental principle of Sensing-as-a-Service and enables on-demand network access to a shared pool of sensing hardware and software that can jointly sense urban physical/social environments. The major conceptual difference of MCS from existing mobile sensing paradigms lies in the introduction of MCS providers, each having virtually owned an extremely large sensing infrastructure comprising heterogeneous smartphones and tablets (called sensing agents). Upon request from a smart-city application which may demand realtime or historical sensed data, an MCS provider either coordinates its distributed sensing agents to jointly perform the sensing task and then return the sensed data or respond with historical sensed data previously collected from its sensing agents.
This project is to investigate several fundamental security and privacy challenges associated with MCS. Specifically, there are four main thrusts in this project: (1) developing a suite of secure and privacy-preserving data aggregation primitives; (2) designing differentially-private sensing task assignment mechanisms; (3) developing a unified framework for verifiable query processing via untrusted smart-city service providers; and (4) building a prototype MCS system for validation and evaluation. The project also actively channels the research results into undergraduate and graduate curriculum, provides research experience for undergraduate and under-represented students, and includes outreach activities to K-12, underrepresented, and oversea students.
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 Cloud Sensing (MCS) is a promising paradigm for collaborative information collection and sharing in emerging smart cities. MCS is based on the fundamental principle of Sensing-as-a-Service and enables on-demand network access to a shared pool of sensing hardware and software that can jointly sense urban physical/social environments. In such a system, a MCS provider provides realtime and historical sensed data to smart-city service providers through either dedicated wireless sensor networks and distributed sensing agents. This project seeks to investigate several fundamental security and privacy challenges associated with MCS.
First, secure data aggregation is a key functionality in wireless sensor networks. We have identified a novel enumeration attack against existing secure probabilistic data aggregation, which highlights the detrimental impact of compromised sensor nodes forging their own sensed data. We have also developed the first secure quantile summary aggregation protocol that allows a base station to learn a much more accurate distribution of the sensed data than simple statistics in the presence of malicious attacks. Second, mobile crowdsensing is expected to play a key role in MCS. We have developed a differentially-private auction mechanism to stimulate mobile workers’ participation in crowdsensing while protecting their privacy. We have also developed effective schemes to defend against false-data injection attacks in crowdsensing systems. Third, we have developed several novel mechanisms to allow an end user to verify the integrity, completeness, and freshness of any query result returned by an untrusted smart-city service provider hosting sensed data provided by MCS provider.
Major research results have been disseminated to academia and industry through paper presentations and publications in leading conferences and journals such as IEEE INFOCOM, IEEE CNS, IEEE/ACM IWQoS, and IEEE/ACM Transactions on Networking. A new course module based on the research results of this project has been developed and taught at the University of Delaware. The project has also provided training in network security, machine learning, mobile crowdsourcing, mechanism design, differential privacy, secure data outsourcing, and algorithm design for four PhD students, including two females.
Last Modified: 02/22/2022
Modified by: Rui Zhang
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