.png){kind=icon}
.png){kind=icon}
Return
Distributed Anti-Eavesdropping Fusion Estimation: A Linear Encryption-Decryption Scheme
.png){kind=icon}
PRE
AI
DOI：10.1002/rnc.70145.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper addresses the issue of privacy protection in distributed fusion estimation. Due to the inherent vulnerabilities of wireless transmission, malicious eavesdropping may enable unauthorized access to data transmitted from local sensors to legitimate users, consequently leading to the potential leakage of sensitive information. To mitigate this risk, a new kind of distributed fusion estimator design method is proposed, which incorporates privacy protection through a linear encryption mechanism. Within the design, the optimal fusion estimator is derived based on the minimum mean square error criterion. By strategically designing the encryption parameters, the estimation error covariance matrix for legitimate users is guaranteed to remain bounded, while the error metrics for eavesdroppers diverge, thereby effectively mitigating the risk of eavesdropping attacks. Finally, a trajectory-tracking simulation is conducted to illustrate the effectiveness of the proposed distributed fusion estimator.
Keywords:
distributed fusion estimation
eavesdropping attack
encryption-decryption scheme
privacy protection
Journal
IF:
3.2
Papers:
6.9K
Citations:
1.4W