.png){kind=icon}
Return
Privacy-Constrained Parallel Distributed Neyman-Pearson Test
.png){kind=icon}
OA
AI
DOI：10.1109/TSIPN.2016.2623092.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, the privacy leakage problem in an eavesdropped parallel distributed binary hypothesis test network is considered. A novel Neyman-Pearson test-operational privacy leakage measure is proposed and a privacy-constrained distributed Neyman-Pearson test problem is formulated. Such privacy-constrained distributed Neyman-Pearson test network is designed to optimize the Neyman-Pearson test performance and meanwhile to satisfy a desired suppression constraint on the privacy leakage. This study characterizes the privacy-constrained distributed Neyman-Pearson test network design and particularly identifies the sufficiency of deterministic likelihood-ratio test for optimality. These results help to simplify the optimal design problem of a privacy-constrained distributed Neyman-Pearson test network. Numerical results are presented to show the trade-off between the test performance and privacy leakage in privacy-constrained distributed Neyman-Pearson test networks.
Keywords:
Cyber-physical system
eavesdropper
likelihood-ratio test
person-by-person optimality
physical-layer secrecy
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.9
Papers:
736
Citations:
1.9K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSave
View more