Machine Learning and Evolutionary Computation in the Age of Data Privacy

Dear Colleagues,

The rapid growth of data-driven technologies has transformed how we approach Machine Learning (ML) and Evolutionary Computation (EC). Furthermore, this advancement has raised new concerns about data privacy and security. This Special Issue seeks to address the crucial intersection of these fields, exploring how we can integrate privacy-preserving techniques with ML and EC methods while maintaining their effectiveness.

This Special Issue will focus on key approaches such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation. The Issue will investigate how these methods integrate with evolutionary algorithms, neural networks, and other computational intelligence paradigms.

The scope encompasses theoretical foundations, algorithmic innovations, and practical implementations of privacy-preserving ML and EC algorithms. We welcome contributions addressing privacy–utility trade-offs in data engineering and optimization. Specifically, topics of interest include, but are not limited to, the following:

• Privacy-preserving data mining;
• Privacy-preserving data publishing;
• Data anonymization;
• Large language models (LLMs) for privacy;
• Differential privacy;
• Federated learning;
• Multi-objective optimization;
• Large-scale optimization;
• Secure multi-party computation;
• Homomorphic encryption;
• Privacy-aware feature engineering and selection.

Real-world applications are particularly encouraged, including healthcare data analysis, financial services, smart city infrastructure, autonomous systems, bioinformatics, IoT networks, and edge computing scenarios.

This Special Issue seeks to advance privacy-preserving computational intelligence by bringing together researchers from ML, EC, cryptography, and privacy communities. We aim to foster interdisciplinary collaboration and establish new research directions that balance computational efficiency, model accuracy, and privacy protection. Further, our focus aims to demonstrate how privacy constraints drive innovation in algorithm design and system architecture. The Issue thus builds upon existing surveys and theoretical works by presenting novel algorithms, empirical studies, and real-world case studies that showcase practical implementations of privacy-preserving computational intelligence.

We invite original research articles that contribute to this rapidly evolving field and help shape the future of responsible AI and computational intelligence.

Dr. Yong-Feng Ge
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

• computational intelligence
• evolutionary computation
• machine learning
• deep learning
• privacy preservation
• privacy–utility trade-off
• large language models
• real-world applications