Mathematical Methods and Models in Information Security with Applications

Dear Colleagues,

In the rapidly evolving digital world, the importance of information security and machine learning cannot be overstated. Using mathematical models in machine learning within this domain is pivotal in enhancing data protection, ensuring secure communication channels, and thwarting cyber attacks. Mathematical models provide a rigorous framework for analyzing security algorithms, assessing vulnerabilities, and quantifying the degree of security. Moreover, the integration of mathematical models ensures that security systems possess desired properties such as confidentiality, authenticity, and non-repudiation. This Special Issue aims to explore innovative methodologies, fundamental theories, and practical applications that address evolving challenges in security and machine learning, offering new insights into the ways in which mathematical models can be applied to enhance the security and resilience of systems.

The scope of this Special Issue encompasses a wide range of topics related to mathematical models in information security and machine learning. Research areas may include (but are not limited to) the following:

• Mathematical foundations of machine learning in information security;
• Security protocols and algorithms based on machine learning with mathematical optimization;
• Secure multiparty computation incorporating machine learning techniques and mathematical models;
• Post-quantum cryptography and machine learning with mathematical theories;
• Cryptanalysis and attacks using machine learning methods supported by mathematical analysis;
• Privacy-enhancing technologies powered by machine learning and mathematical cryptography;
• Authentication and anonymity protocols incorporating machine learning and mathematical models;
• Security and privacy in federated learning, crowdsourcing, and cloud computing using machine learning approaches with mathematical foundations;
• Security considerations in emerging technologies such as IoT and blockchain enhanced by machine learning and mathematical models.

Dr. Guodong Li
Dr. Maofa Wang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• information security
• machine learning
• mathematical models
• security and privacy
• privacy-enhancing technologies