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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
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Secure State Estimation for Artificial Neural Networks With Unknown-But-Bounded Noises: A Homomorphic Encryption
IEEE Transactions on Neural Networks and Learning Systems
|April 24, 2024
Summary
This study introduces a secure state estimation method for artificial neural networks (ANNs) using homomorphic encryption (HES). It enables secure estimation over limited bandwidth networks without data decryption, ensuring data integrity.
Area of Science:
- Control Systems Engineering
- Cybersecurity
- Artificial Intelligence
Background:
- Secure state estimation is crucial for systems with noisy data and limited communication.
- Artificial neural networks (ANNs) are increasingly used but require secure data handling.
- Open, bandwidth-limited networks pose challenges for transmitting sensitive measurement data.
Purpose of the Study:
- To develop a secure state estimation algorithm for ANNs under unknown-but-bounded noises.
- To ensure data security during transmission over bandwidth-limited networks using novel encryption.
- To enable state estimation directly from encrypted data without decryption.
Main Methods:
- A novel homomorphic encryption scheme (HES) combining encoding-decoding mechanism (EDM) and Paillier encryption.
- Development of a secure set-membership state estimation algorithm operating on encrypted data.
- Derivation of secure state estimator gains using optimization and Lagrange multiplier method.
Main Results:
- Sufficient conditions for the existence of an ellipsoidal set under noise and HES constraints were determined.
- The proposed secure state estimation algorithm effectively computes estimates from encrypted data.
- The method ensures data security throughout the estimation process.
Conclusions:
- The developed secure state estimation approach is effective for ANNs in noisy, bandwidth-limited environments.
- The homomorphic encryption scheme provides robust data protection during transmission and estimation.
- This work advances secure and reliable state estimation in networked control systems.
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