Award Abstract # 2229427
Collaborative Research: SWIFT: Closing the Loop for Accountable Interference-free Spectrum Sharing with Passive Radio Receivers

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: WASHINGTON UNIVERSITY, THE
Initial Amendment Date: August 31, 2022
Latest Amendment Date: August 31, 2022
Award Number: 2229427
Award Instrument: Standard Grant
Program Manager: Alhussein Abouzeid
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2022
End Date: September 30, 2026 (Estimated)
Total Intended Award Amount: $630,521.00
Total Awarded Amount to Date: $630,521.00
Funds Obligated to Date: FY 2022 = $630,521.00
History of Investigator:
  • Neal Patwari (Principal Investigator)
    neal.patwari@utah.edu
  • Ning Zhang (Co-Principal Investigator)
Recipient Sponsored Research Office: Washington University
1 BROOKINGS DR
SAINT LOUIS
MO  US  63130-4862
(314)747-4134
Sponsor Congressional District: 01
Primary Place of Performance: Washington University
ONE BROOKINGS DR
SAINT LOUIS
MO  US  63130-4862
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): L6NFUM28LQM5
Parent UEI:
NSF Program(s): SWIFT-Spectrum Innov Futr Tech,
SII-Spectrum Innovation Initia
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7976, 1207
Program Element Code(s): 140Y00, 151Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041, 47.049, 47.070

ABSTRACT

Spectrum in the US is incredibly valuable to science, education, commerce, transportation, and contemporary life. Wireless bandwidth needs are rapidly growing, but allocation of spectrum to wireless users should not come at the expense of scientific observation in astronomy and earth science which are already under-allocated and subject to interference when operating opportunistically outside the narrow protected bands. This project is developing new secure and accountable sharing protocols that not only enable more efficient sharing of the spectrum between terrestrial commercial wireless systems and passive receivers, but also empowers passive systems to force a particular interfering transmitter to switch band. Improved coexistence allows more reliable operation of radio astronomy receivers. This project is creating open source software and data, and working with the ITU-R to ensure that results impact the research community and future spectrum sharing policy. Developments are impacting undergraduate and graduate education through course material and research experiences, and the project is engaged in K12 outreach.

This project is developing RF watermarks that embed random pseudonyms into transmitted wireless communication signals so that passive receivers can demodulate the pseudonym of any interferer. The proposed system then allows passive receivers to indirectly inform the interfering device to change band. To prevent any other device from inferring private information, the proposed system leverages differential privacy to quantitatively limit privacy leakage. Further, the project is adapting software attestation to develop proof of correct execution of spectrum decision on user equipment, and complementing the watermark-based detection system with spectrum policy enforcement. Protocols under study are being implemented and tested on PAWR testbeds as an open source project. Extensive experimentation, including at the Owens Valley Radio Observatory, is validating the technical contributions, and quantifying its performance and robustness to attacks.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Liu, Han and Wu, Yuhao and Yu, Zhiyuan and Zhang, Ning "Please Tell Me More: Privacy Impact of Explainability through the Lens of Membership Inference Attack" 2024 IEEE Symposium on Security and Privacy (SP) , 2020 Citation Details
Liu, Han and Wu, Yuhao and Yu, Zhiyuan and Vorobeychik, Yevgeniy and Zhang, Ning "SlowLiDAR: Increasing the Latency of LiDAR-Based Detection Using Adversarial Examples" 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023 https://doi.org/10.1109/CVPR52729.2023.00498 Citation Details
Liu, Han and Wu, Yuhao and Zhai, Shixuan and Yuan, Bo and Zhang, Ning "RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation with Natural Prompts" 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023 https://doi.org/10.1109/CVPR52729.2023.01972 Citation Details
Wang, Jinwen and Wang, Yujie and Li, Ao and Xiao, Yang and Zhang, Ruide and Lou, Wenjing and Hou, Y. Thomas and Zhang, Ning "ARI: attestation of real-time mission execution integrity" Proceedings of the 32nd USENIX Conference on Security Symposium , 2023 Citation Details
Wang, Jinwen and Wu, Yuhao and Liu, Han and Yuan, Bo and Chamberlain, Roger and Zhang, Ning "IP Protection in TinyML" 2023 60th ACM/IEEE Design Automation Conference (DAC) , 2023 https://doi.org/10.1109/DAC56929.2023.10247898 Citation Details
Wang, Y and Mack, C and Tan, X and Zhang, N and Zhao, Z and Baruah, S and Ward, B "InsectACIDE: Debugger-Based Holistic Asynchronous CFI for Embedded Systems" Proceedings of the 30th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'24) , 2024 https://doi.org/10.1109/RTAS61025.2024.00036 Citation Details

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