.png){kind=icon}
返回
Efficient Artificial Intelligence With Novel Matrix Transformations and Homomorphic Encryption
.png){kind=icon}
PRE
AI
DOI：10.1109/JETCAS.2024.3466849.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Artificial intelligence
Computational modeling
Data models
Training
Cryptography
Predictive models
Mathematical models
homomorphic encryption
matrix transformation
convergence analysis
期刊
IF:
3.8
论文数:
1.4K
被引数:
2.8K
机构
引用论文
Publicly Verifiable Secure Multi-Party Computation Framework Based on Bulletin Board基于公告板的可公开验证的安全多方计算框架
PREAI
分享收藏
Optimized Privacy-Preserving CNN Inference With Fully Homomorphic Encryption基于全同态加密的优化隐私保护CNN推理
PREAI
分享收藏Privacy-Preserving Machine Learning Using Functional Encryption: Opportunities and Challenges使用功能加密的隐私保护机器学习: 机遇与挑战
OAAI
OAAI分享收藏Communication-Efficient Secure Computation of Encrypted Inputs Using (k, n) Threshold Secret Sharing.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容