Efficient Artificial Intelligence With Novel Matrix Transformations and Homomorphic Encryption

This paper addresses the challenges of data privacy and computational efficiency in artificial intelligence (AI) models by proposing a novel hybrid model that combines homomorphic encryption (HE) with AI to enhance security while maintaining learning accuracy. The novelty of our model lies in the in...

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Bibliographic Details
Published in:IEEE journal on emerging and selected topics in circuits and systems 2024-12, Vol.14 (4), p.717-728
Main Authors: Bao Phan, Quoc, Nguyen, Tuy Tan
Format: Article
Language:English
Subjects:
Accuracy
Adaptive algorithms
Air pollution
Artificial intelligence
Computational efficiency
Computational modeling
convergence analysis
Cryptography
Data models
Data storage
Efficiency
Encryption
Forecasting
Forest fire detection
homomorphic encryption
Machine learning
Mathematical models
matrix transformation
Pollution detection
Predictive models
Security
Training
Transformations (mathematics)
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Summary:This paper addresses the challenges of data privacy and computational efficiency in artificial intelligence (AI) models by proposing a novel hybrid model that combines homomorphic encryption (HE) with AI to enhance security while maintaining learning accuracy. The novelty of our model lies in the introduction of a new matrix transformation technique that ensures compatibility with both HE algorithms and AI model weight matrices, significantly improving computational efficiency. Furthermore, we present a first-of-its-kind mathematical proof of convergence for integrating HE into AI models using the adaptive moment estimation optimization algorithm. The effectiveness and practicality of our approach for training on encrypted data are showcased through comprehensive evaluations of well-known datasets for air pollution forecasting and forest fire detection. These successful results demonstrate high model performance, with nearly 1 R-squared for air pollution forecasting and 99% accuracy for forest fire detection. Additionally, our approach achieves a reduction of up to 90% in data storage and a tenfold increase in speed compared to models that do not use the matrix transformation method. Our primary contribution lies in enhancing the security, efficiency, and dependability of AI models, particularly when dealing with sensitive data.
ISSN:2156-3357
2156-3365
DOI:10.1109/JETCAS.2024.3466849