Privacy-Preserving and Secure Machine Learning
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".
Deadline for manuscript submissions: 1 February 2027 | Viewed by 2
Special Issue Editors
Interests: trust and access control in smart environments; software engineering; usable security; mobile computing; applied machine learning, and privacy
Special Issues, Collections and Topics in MDPI journals
Interests: biometric authentication and identification; cybersecurity; machine learning; secure software engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid proliferation of machine learning (ML) in vital domains such as healthcare, finance, intelligent environments, and mobile computing has raised significant concerns about data privacy, security, and reliability. ML models predominantly rely on large-scale, often sensitive data, and traditional approaches to model training and deployment expose systems to risks such as data leakage, model inversion, membership inference attacks, and adversarial manipulation. This Special Issue seeks to examine novel methodologies and frameworks that promote privacy-preserving and secure machine learning. The scope of this Special Issue includes, but is not limited to, the following topics:
- Federated Learning for Privacy Preservation
- Differential privacy
- Homomorphic encryption
- Secure multi-party computation
- Robust model design against adversarial threats.
Furthermore, the issue welcomes interdisciplinary research that integrates ML security and privacy considerations into, for example, software engineering practices, usability studies, trust management, etc. This issue also welcomes comprehensive systematic literature surveys that follow robust guidelines such as those of PRISMA.
Authors are encouraged to present novel architectures, empirical evaluations, real-world applications, and regulatory or ethical perspectives that enhance the reliability and adoption of secure ML systems. The goal of this Special Issue is to provide researchers and practitioners with a comprehensive platform for advancing the development of trustworthy, privacy-aware, and secure intelligent systems.
Dr. Ali Ahmed
Dr. Tarek Gaber
Guest Editors
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly 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 1800 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.
Keywords
- privacy-preserving machine learning
- secure machine learning
- federated learning for privacy preservation
- differential privacy
- homomorphic encryption
- secure multi-party computation
- adversarial machine learning
- trustworthy AI
- data security and privacy
- usable security
- security and explainable AI
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