Remote state estimation with privacy against active eavesdroppers

This paper considers a cyber–physical system under an active eavesdropping attack. A remote legitimate user estimates the state of a linear plant from the state information received from a sensor. Transmissions from the sensor occur via an insecure and unreliable network. An active eavesdropper may perform an attack during system operation. The eavesdropper intercepts transmissions from the sensor, whilst simultaneously sabotaging the data transfer from the sensor to the remote legitimate user to harm its estimation performance. To maintain state confidentiality, we propose an encoding scheme that is activated on the detection of an eavesdropper. Our scheme transmits noise based on a pseudo-random indicator, pre-arranged at the legitimate user and sensor. The transmission of noise harms the eavesdropper’s performance, more than that of the legitimate user. Using the proposed encoding scheme, we impair the eavesdropper’s expected estimation performance, whilst minimizing expected performance degradation at the legitimate user. We explore the trade-off between state confidentiality and legitimate user performance degradation by selecting the probability that the sensor transmits noise. Under certain design choices, the trace of the expected estimation error covariance of the eavesdropper is greater than that of the legitimate user. Numerical examples are provided to illustrate the proposed encoding scheme.

Authors

Institutions

Publication Details

Journal
Automatica
Published
2024-10-02
DOI
https://doi.org/10.1016/j.automatica.2024.111932
Citations
9
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
2.95
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Remote state estimation with privacy against active eavesdroppers

Justin M. Kennedy, Daniel E. Quevedo, Matthew J. Crimson
9 citations
Automatica
Smart Grid Security and Resilience
2.95
article

Remote state estimation with privacy against active eavesdroppers

Justin M. Kennedy, Daniel E. Quevedo, Matthew J. Crimson
article en
9 citations

Abstract

This paper considers a cyber–physical system under an active eavesdropping attack. A remote legitimate user estimates the state of a linear plant from the state information received from a sensor. Transmissions from the sensor occur via an insecure and unreliable network. An active eavesdropper may perform an attack during system operation. The eavesdropper intercepts transmissions from the sensor, whilst simultaneously sabotaging the data transfer from the sensor to the remote legitimate user to harm its estimation performance. To maintain state confidentiality, we propose an encoding scheme that is activated on the detection of an eavesdropper. Our scheme transmits noise based on a pseudo-random indicator, pre-arranged at the legitimate user and sensor. The transmission of noise harms the eavesdropper’s performance, more than that of the legitimate user. Using the proposed encoding scheme, we impair the eavesdropper’s expected estimation performance, whilst minimizing expected performance degradation at the legitimate user. We explore the trade-off between state confidentiality and legitimate user performance degradation by selecting the probability that the sensor transmits noise. Under certain design choices, the trace of the expected estimation error covariance of the eavesdropper is greater than that of the legitimate user. Numerical examples are provided to illustrate the proposed encoding scheme.

AutomaticaVol. 171
Queensland University of Technology (AU)
Gender equality
Openalex Percentile: Top 8%
Smart Grid Security and Resilience
2.95
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.