Dear Colleagues,

The widespread use of information and communication technologies has revolutionized our lives, offering unprecedented convenience and connectivity. However, this progress has also introduced significant challenges to privacy preservation and system security. Machine learning techniques have emerged as powerful tools capable of addressing the evolving security and privacy challenges.

This Special Issue of Applied Sciences (MDPI) Journal on “Privacy-Preserving and System Security Control Based on Machine Learning” invites original research contributions and review articles to explore the latest advancements and applications of machine learning for privacy preservation and system security.

We welcome submissions that cover a broad range of topics, including but not limited to the following:

- Privacy-preserving machine learning algorithms;
- Differential privacy;
- Adversarial machine learning for security control;
- Anomaly detection using machine learning for system security;
- Zero-day attack detection using machine learning;
- Secure federated learning;
- Privacy-enhancing technologies in machine learning.

Applied Sciences (MDPI) is an open-access journal that is free for readers, with article processing charges (APC) paid by authors or their institutions. The papers are indexed within Scopus, SCIE (Web of Science), Inspec,
CAPlus / SciFinder, and other databases.
The Applied Sciences (MDPI) journal ranking is very good: JCR—Q1 (Engineering, Multidisciplinary) / CiteScore—Q1.
(General Engineering).

We expect this Special Issue to provide a timely and significant platform for researchers and practitioners to present their latest findings and foster collaborations in this swiftly advancing field.
We are looking forward to your contributions!

Guest Editors,
Dr. Emil Pricop, Petroleum-Gas University of Ploiesti, Ploiesti, Romania
Dr. Jaouhar Fattahi, Laval University, Quebec City, Canada
Dr. Grigore Stamatescu, National University of Science and Technology Politehnica of Bucharest, Romania