Proceedings of The 1st International Workshop on Security and Privacy-Preserving AI/ML
SPAIML 2025
Jens Leicht University of Duisburg-Essen, Germany Malte Josten University of Duisburg-Essen, Germany
Holger Schmidt Dortmund University of Applied Sciences and Arts, Germany Kimberly Cornell University of Albany, USA Lorenz Schwittmann Independent Researcher, Germany Maritta Heisel University of Duisburg-Essen, Germany Meiko Jensen Karlstad University, Sweden Nicolas Diaz-Ferreyra Hamburg University of Technology,
Germany Oliver Hahm Frankfurt University of Applied Sciences,
Germany Razvan Beuran Japan Advanced Institute of Science and
Technology, Japan Simone Fischer-Hu¨bner Karlstad University, Sweden Stefen Bondorf Ruhr University Bochum, Germany Stephan Sigg Aalto University, Finland Torben Weis University of Duisburg-Essen, Germany Vadim Safronov University of Oxford, UK
Zolatn´ Mann University of Halle, Germany
Jens Leicht University of Duisburg-Essen, Germany Julien Lukasewycz University of Duisburg-Essen, Germany Malte Josten University of Duisburg-Essen, Germany Torben Weis University of Duisburg-Essen, Germany https://spaiml.com/2025 Copyright © 2025 for the individual papers by the papers’ authors. Copyright © 2025 for the volume as a collection by its editors.
This volume and its papers are published under the
Creative Commons License Attribution 4.0 International (CC BY 4.0) The workshop focuses on the transformative potential of AI/ML technologies in addressing key challenges in security and privacy across diverse domains. In an era of increasing digitalization and interconnectedness, organizations face evolving threats, from sophisticated cyberattacks to complex data privacy concerns. Traditional methods often struggle to adapt to the dynamic nature of these challenges, particularly in scenarios requiring real-time analysis, anomaly detection, and large-scale data management. AI/ML presents a paradigm shift by enabling intelligent, scalable, and proactive approaches to security and privacy. For instance, machine learning models can detect patterns in network trafic indicative of cyberattacks, while AI-driven solutions can enable privacy-preserving data processing through federated learning or diferential privacy techniques. By focusing on how AI/ML can be harnessed to safeguard sensitive data and systems across various domains, this workshop aims to advance the state of the art in security and privacy.
This year’s workshop (https://spaiml.com/2025) took place on October 26th, 2025 in conjunction with the 28th European Conference on Artificial Intelligence ( https://www.ecai2025.org/) in Bologna, Italy.
The double-blind review process involved 14 PC members whose afiliations came from 6 diferent countries: Finland (1), Germany (8), Japan (1), Sweden (2), United Kingdom (1), USA (1). We received a total of 10 submissions from authors based in 9 diferent countries: Austria (1), Finland (1), Germany (4), Italy (4), Japan (4), Netherlands (6), Sweden (2), United Kingdom (6), United States (4). Out of these 10 submissions, 7 were accepted for presentation at the workshop and inclusion in these proceedings as regular papers.
In addition to two paper sessions, this year’s workshop hosted a keynote by Victor Morel, titled ”Should I Trust It With My Data? Capabilities, Limits, and Perspectives of AI Technologies for Privacy.”
We want to thank all authors for their submissions, the PC members for their eforts in reviewing the papers, and the keynote speaker for their inspiring talk.