Special Issue "Privacy and Security in Machine Learning"

Special Issue Editors

Guest Editor
Prof. Dr. Edgar Weippl

1. SBA Research, Vienna 1040, Austria
2. Department of Computer Science and Security, University of Applied Sciences St. Pölten, St. Pölten 3100, Austria
Website1 | Website2 | E-Mail
Interests: (4) information security
Guest Editor
Prof. Dr. Francesco Buccafurri

Department of Information Engineering, Infrastructures and Sustainable Energy (DIIES), University Mediterranea of Reggio Calabria, Reggio Calabria 89122, Italy
Website1 | Website2 | E-Mail
Interests: (1, 4, 5) cybersecurity; trust; privacy; cloud security; security in e-government

Special Issue Information

Dear Colleagues,

Machine learning is clearly a research area that will continue creating real-world impacts, as computing power becomes increasingly more readily available. Security and privacy considerations, however, are vital, in particular since machine learning algorithms are often perceived as magical black boxes, in which the inner workings are not easily made transparent. Important topics that warrant new research are, among others:

  • The right to be forgotten. How much of the “original” personal data is embedded in trained neural networks? Can we delete this data without retraining? How can we measure the anonymity/pseudonymity of training data embedded in a trained network?
  • How easy is it to attack training sets and trained networks? If ML is used for real-world applications such as autonomous driving, successful attacks may have huge impact.

We look forward to receiving research papers that address, not only the aforementioned examples, but also any excellent research that investigates privacy and security aspects in ML in depth.

Prof. Dr. Edgar Weippl
Prof. Dr. Francesco Buccafurri
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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

  • Threat models
  • Attacks against machine learning
  • Malware detection
  • Black-box attacks against machine learning
  • Adversarial training and defensive distillation
  • Privacy-preserving machine learning
  • Application of machine learning to security and privacy

Published Papers

This special issue is now open for submission.
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