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Secure Deep Learning for Intelligent Terahertz Metamaterial Identification.

Feifei Liu¹, Weihao Zhang¹, Yu Sun¹

¹School of Cyber Science and Technology, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|October 8, 2020

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This study introduces a secure method for detecting metamaterials using a crypto-oriented convolutional neural network (CNN). The novel approach ensures data privacy through homomorphic encryption, achieving 100% accuracy in identifying metamaterials.

Area of Science:

Metamaterials science
Artificial intelligence
Cryptography

Background:

Metamaterials possess unique properties for interdisciplinary applications.
Detecting and identifying metamaterials, especially in mixtures, is a critical but unexplored research area.
Stealthy monitoring poses a significant threat, necessitating secure identification methods.

Discussion:

A novel crypto-oriented convolutional neural network (CNN) is proposed for secure metamaterial detection.
Homomorphic encryption is utilized to encrypt terahertz signals, allowing direct processing of ciphertexts by the CNN.
The CNN model achieved 100% accuracy on test sets, surpassing human and traditional machine learning performance.

Key Insights:

The developed CNN model demonstrates perfect accuracy in identifying metamaterials from encrypted terahertz signals.

Keywords:

deep learning homomorphic encryption metamaterial identification private preserving terahertz time domain spectroscopy (THz-TDS)

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The system ensures data privacy, as only the data owner can decrypt the results.

The method offers a secure, AI-driven paradigm for private-preserving material identification.

Outlook:

This approach paves the way for secure AI-based identification of advanced materials.
Further research can explore broader applications of crypto-oriented CNNs in material science.
The integration of encryption and AI offers a robust solution against unauthorized monitoring.