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Related Concept Videos

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
State Function, Exact and Inexact Differentials01:27

State Function, Exact and Inexact Differentials

A state function is a thermodynamic property that depends solely on the current state of a system, irrespective of its history or how it arrived at that state. These functions are represented by capital letters, such as U, H, and S, which stand for internal energy, enthalpy, and entropy, respectively.For instance, the value of internal energy depends on the system's state variables and remains unaffected by the process path. This means that whether the system underwent a linear process or a...
Separable Differential Equations01:20

Separable Differential Equations

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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

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Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

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Related Experiment Videos

Partial-encryption-decryption-based secure state estimation of singularly perturbed complex networks: A Paillier

Yunjie Chen1, Zidong Wang2, Yurong Liu1

  • 1Department of Mathematics, Yangzhou University, Yangzhou, 225002, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 29, 2026
PubMed
Summary

This study introduces a secure state estimation method for complex networks using partial encryption-decryption (PED) to balance data security and computational efficiency. The novel approach ensures resilient estimation against perturbations and eavesdropping.

Keywords:
Mean-square boundednessPaillier encryptionPartial encryption-decryptionResilient estimationSecure state estimationSingularly perturbed complex networks

Related Experiment Videos

Area of Science:

  • Control Systems Engineering
  • Network Security
  • Information Theory

Background:

  • Secure state estimation is crucial for complex networks transmitting data over open channels.
  • Existing methods face challenges in balancing data security with computational efficiency.
  • Perturbations in estimator gains and measurement quantization can degrade estimation performance.

Purpose of the Study:

  • To develop a secure and resilient state estimation technique for discrete-time singularly perturbed complex networks.
  • To propose a novel Paillier-based partial encryption-decryption (PED) mechanism for enhanced data protection.
  • To design an estimator that robustly handles gain perturbations and quantization errors.

Main Methods:

  • Integration of probabilistic measurement quantization with Paillier homomorphic encryption for partial encryption-decryption (PED).
  • Development of a group-based round-robin protocol for selecting measurement subsets for encryption.
  • Application of Lyapunov stability theory to derive conditions for estimation error boundedness.
  • Characterization of estimator gains using matrix inequalities for a tractable design.

Main Results:

  • The proposed PED mechanism achieves a tradeoff between data security and computational efficiency.
  • Sufficient conditions are derived to guarantee exponential ultimate boundedness of estimation errors in the mean-square sense.
  • A computationally tractable procedure for designing the resilient state estimator is presented.

Conclusions:

  • The novel secure state estimation scheme effectively enhances data security while maintaining computational efficiency.
  • The designed estimator demonstrates robustness against estimator gain perturbations and quantization errors.
  • Numerical validation confirms the effectiveness and resilience of the proposed approach for complex networks.