@inproceedings {INPROC-2026-01,
   author = {Andrea Fieschi and Christoph Stach and Pascal Hirmer},
   title = {{AnonymEx: An Interactive Platform for Exploring and Evaluating Anonymization Techniques through Re-identification Attacks}},
   booktitle = {Proceedings 29th International Conference on Extending Database Technology (EDBT 2026), Tampere, Finland, March 24 - March 27 (Number 3)},
   editor = {Wolfgang Lehner and Vanessa Braganholo and Kostas Stefanidis and Zheying Zhang and Alexander Krause and Jo{\~a}o Felipe Nicolaci Pimentel},
   publisher = {OpenProceedings.org},
   institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
   series = {Advances in Database Technology},
   volume = {29},
   pages = {704--707},
   type = {Demonstration},
   month = {M{\"a}rz},
   year = {2026},
   isbn = {978-3-98318-104-9},
   issn = {2367-2005},
   doi = {10.48786/edbt.2026.60},
   keywords = {anonymization; re-identification; empirical evaluation; knowledge graph; demo},
   language = {Englisch},
   cr-category = {K.4.1 Computers and Society Public Policy Issues},
   contact = {Senden Sie eine E-Mail an \<andrea.fieschi@ipvs.uni-stuttgart.de\>.},
   department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
   abstract = {As data collection and analysis continue to expand, the need for effective and
      transparent anonymization becomes increasingly critical. Yet the growing
      diversity of anonymization techniques makes it difficult for developers to
      evaluate their guarantees and choose an appropriate method at design time. We
      present AnonymEx, an interactive platform that enables developers to
      empirically compare anonymization techniques using a unified metric: their
      susceptibility to re-identification attacks. The platform integrates (i) a
      literature-grounded knowledge graph linking anonymization techniques to
      documented re-identification attacks, (ii) executable, containerized
      implementations of both techniques and attacks, and (iii) an assistant that
      supports exploratory learning and helps users identify candidate techniques
      based on their requirements. During the demonstration, attendees explore the
      anonymization landscape, test techniques using provided datasets, run the
      associated attacks, and assess the assistant's suggestions within the
      Anonymization-by-Design workflow. AnonymEx thus provides a practical,
      transparent, and reproducible sandbox for design-time decision-making and
      cross-category empirical evaluation of anonymization techniques.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2026-01&engl=0}
}

@article {ART-2026-01,
   author = {Christoph Stach and Cl{\'e}mentine Gritti and Iouliana Litou},
   title = {{Special Issue on Security and Privacy in Blockchains and the IoT—3rd Edition}},
   journal = {Future Internet},
   address = {Basel, Schweiz},
   publisher = {MDPI},
   volume = {18},
   number = {2},
   pages = {1--8},
   type = {Artikel in Zeitschrift},
   month = {Februar},
   year = {2026},
   issn = {1999-5903},
   doi = {10.3390/fi18020090},
   language = {Englisch},
   cr-category = {D.4.6 Operating Systems Security and Protection,
                   K.4.1 Computers and Society Public Policy Issues,
                   K.6.5 Security and Protection},
   ee = {https://www.mdpi.com/1999-5903/18/2/90},
   contact = {Senden Sie eine E-Mail an \<Christoph.Stach@ipvs.uni-stuttgart.de\>.},
   department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
   abstract = {This Special Issue presents a curated collection of advanced research
      contributions addressing security, privacy, and trust in the convergence of
      blockchain technologies and the Internet of Things. As these technologies
      increasingly underpin data-driven and autonomous systems in domains such as
      healthcare, industrial infrastructures, and smart homes, ensuring their
      resilience and trustworthiness has become a critical research and engineering
      challenge.
      
      The contributions assembled in this Special Issue reflect the maturity of the
      field by combining foundational analyses with practical, system-oriented
      solutions. Topics range from structured threat models, vulnerability analyses,
      and privacy challenges in decentralized environments to intelligent detection
      mechanisms based on machine learning, explainable artificial intelligence, and
      federated learning. The Special Issue also provides a comprehensive overview
      how blockchain based security mechanisms can be integrated into real world,
      security critical applications, addressing requirements such as data integrity,
      auditability, accountability, and regulatory compliance.
      
      A pivotal characteristic of this Special Issue is its explicit emphasis on
      trust as a unifying concept that links security mechanisms, privacy-preserving
      designs, and data-centric decision-making. By bridging theoretical rigor with
      experimentally validated systems and applied case studies, this Special Issue
      offers researchers and practitioners a coherent and timely perspective on the
      evolving challenges and solutions for secure, privacy preserving, and
      trustworthy data ecosystems.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2026-01&engl=0}
}