Mathematical Models and Deep Learning Algorithm for Encryption Computation
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 10 November 2025 | Viewed by 55
Special Issue Editor
Special Issue Information
Dear Colleagues,
As the demand for secure data processing continues to grow, traditional cryptographic and privacy-preserving methods face increasing challenges related to efficiency and scalability. These challenges are particularly evident in fields such as financial transactions, multimedia processing, and natural language processing, where both robust security and high performance are essential. To address these issues, the integration of advanced mathematical models and deep learning techniques has become crucial for advancing encryption and privacy-preserving technologies.
This Special Issue focuses on the latest developments in the fusion of mathematical methods and deep learning with encryption technologies, with the goal of fostering innovations that improve both the security and computational efficiency of encryption systems in real-world applications.
This Special Issue welcomes topics including (but not limited to) the following:
- Mathematical models in encryption technology;
- Deep learning techniques for encryption technology;
- Homomorphic encryption and privacy-preserving computation;
- Cryptanalysis using machine learning algorithms;
- Privacy-preserving deep learning;
- Optimized encryption methods for big data and AI applications;
- Other topics related to mathematical approaches and encryptions.
We look forward to contributions that explore the integration of mathematical approaches and deep learning to push the boundaries of encryption technology and secure data handling.
Dr. Peijia Zheng
Guest Editor
Manuscript Submission Information
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Keywords
- mathematical approaches
- deep learning models
- encryption technology
- privacy-preserving computation
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