Award Abstract # 1718078
SaTC: CORE: Small: Secure Mobile Cloud Sensing

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: UNIVERSITY OF DELAWARE
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: FY 2017 = $500,000.00
History of Investigator:
  • Rui Zhang (Principal Investigator)
    ruizhang@udel.edu
Recipient Sponsored Research Office: University of Delaware
550 S COLLEGE AVE
NEWARK
DE  US  19713-1324
(302)831-2136
Sponsor Congressional District: 00
Primary Place of Performance: University of Delaware
18 Amstel Ave, 448 Smith Hall
Newark
DE  US  19716-2599
Primary Place of Performance
Congressional District:
00
Unique Entity Identifier (UEI): T72NHKM259N3
Parent UEI:
NSF Program(s): Secure &Trustworthy Cyberspace
Primary Program Source: 01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 025Z, 7434, 7923, 9150
Program Element Code(s): 806000
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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(Showing: 1 - 10 of 19)
Aseeri, Aishah and Zhang, Rui "Locally Differentially Private Quantile Summary Aggregation in Wireless Sensor Networks" Asian Conference on Intelligent Information and Database Systems , 2022 Citation Details
Aseeri, Aishah and Zhang, Rui "SecQSA: Secure sampling-based quantile summary aggregation in wireless sensor networks" 2021 International Conference on Mobility, Sensing and Networking (MSN) , 2021 Citation Details
Aseeri, Aishah and Zhang, Rui "Secure Data Aggregation in Wireless Sensor Networks: Enumeration Attack and Countermeasure" ICC 2019 - 2019 IEEE International Conference on Communications (ICC) , 2019 10.1109/ICC.2019.8761889 Citation Details
Chen, Yimin and Li, Tao and Zhang, Rui and Zhang, Yanchao and Hedgpeth, Terri "EyeTell: Video-Assisted Touchscreen Keystroke Inference from Eye Movements" 2018 IEEE Symposium on Security and Privacy (SP) , 2018 10.1109/SP.2018.00010 Citation Details
Han, Dianqi and Chen, Yimin and Li, Tao and Zhang, Rui and Zhang, Yaochao and Hedgpeth, Terri "Proximity-Proof: Secure and Usable Mobile Two-Factor Authentication" Proceedings of the 24th Annual International Conference on Mobile Computing and Networking , 2018 10.1145/3241539.3241574 Citation Details
Han, Dianqi and Li, Ang and Zhang, Lili and Zhang, Yan and Li, Jiawei and Li, Tao and Zhang, Rui and Zhang, Yanchao "(In)secure Acoustic Mobile Authentication" IEEE Transactions on Mobile Computing , 2021 https://doi.org/10.1109/TMC.2021.3053282 Citation Details
Hu, Yidan and Yao, Xin and Zhang, Rui and Zhang, Yanchao "Freshness Authentication for Outsourced Multi-Version Key-Value Stores" IEEE Transactions on Dependable and Secure Computing , 2022 https://doi.org/10.1109/TDSC.2022.3172380 Citation Details
Hu, Yidan and Zhang, Rui "A Spatiotemporal Approach for Secure Crowdsourced Radio Environment Map Construction" IEEE/ACM Transactions on Networking , 2020 https://doi.org/10.1109/TNET.2020.2992939 Citation Details
Hu, Yidan and Zhang, Rui "Differentially-Private Incentive Mechanism for Crowdsourced Radio Environment Map Construction" IEEE INFOCOM 2019 - IEEE Conference on Computer Communications , 2019 10.1109/INFOCOM.2019.8737512 Citation Details
Hu, Yidan and Zhang, Rui and Zhang, Yanchao "KV-Fresh: Freshness authentication for outsourced multi-version key-value stores" IEEE International Conference on Computer Communications (INFOCOM) , 2020 Citation Details
Li, Tao and Chen, Yimin and Zhang, Rui and Zhang, Yanchao and Hedgpeth, Terri "Secure crowdsourced indoor positioning systems" Proceedings - IEEE INFOCOM , 2018 Citation Details
(Showing: 1 - 10 of 19)

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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