Towards Privacy Preservation and Data Protection in Information System Design


Call for Papers - Special Issue

Towards Privacy Preservation and Data Protection in Information System Design


  • Agnes Koschmider, University of Kiel, Germany
  • Judith Michael, RWTH Aachen, Germany
  • Nathalie Baracaldo, IBM Research - Almaden, USA

This special issue seeks research articles, industry or experience reports related to all aspects of privacy and data protection in the design of information systems. Data breaches and data misuse continuously increase the uncertainty in the use of devices and systems that process personal data. Apart from this, declarations of consent are given by users without a clear understanding of it. Sensitive information, such as personal data must be given special attention when designing information systems. Personal data protection is of paramount importance to ensure compliance with the General Data Protection Regulation (GDPR), which obliges organizations to consider privacy throughout the complete development process. This requires an interplay between technical solutions related to privacy engineering techniques and user-friendly interfaces allowing to protect data and to configure privacy preferences by users. This is also critical to build information systems that users can trust. This special issue invites submissions that address privacy-by-design, privacy engineering, privacy enhancing technologies, privacy architectures, the integration of privacy into software engineering lifecycle phases, user experience with privacy mechanisms, data protection in analytics and mining or investigating privacy concerns within agile (or) model-based system engineering methods.

Possible topics include but are not limited to:

  • User-centered access control in IS design
  • The relationship between security, privacy and trust in IS design
  • Privacy compliant reuse of software
  • Conceptual modeling, privacy and trust
  • Privacy and trust by design in process-aware information systems
  • Model-based approaches for privacy preserving IS
  • Agile development methods and privacy guarantee
  • User acceptance of privacy-related features and applications
  • Best-practices for privacy regulations in various application domains
  • Privacy preservation and machine learning
  • Privacy engineering for (event) logs

Associate Editors

  • Rizwan Asghar, University of Auckland
  • Achim D. Brucker, University of Exeter, UK
  • Martin Degerling, Ruhr-Universität Bochum
  • Michael Fellmann, Rostock University
  • Wilhelm Hasselbring, Kiel University
  • Fabrizio Maria Maggi, University of Tartu
  • Felix Mannhardt, SINTEF, Norway
  • Raimundas Matulevičius, University of Tartu
  • Günther Pernul, Universität Regensburg
  • Iris Reinhartz-Berger, University of Haifa

Important Dates

  • Publication and Distribution of the CfP: June 2019
  • Deadline for Paper Submission: December 15 2019
  • First round of reviews completed: February 2020
  • Submission of revisions if needed: April 2020
  • Second round of reviews completed: June 2020
  • Final submission: August 2020
  • Publication of Special Issue October 2020

Submissions must adhere to the author guidelines for EMISAJ, which can be found at When submitting the article in the submission system, please make sure to select the section "Special Issue on Privacy in IS Design". Please note that EMISAJ is a True Open Access journal (no author fees, no exclusive rights to publish). EMISAJ is indexed in DBLP, EBSCO, and the Emerging Sources Citation Index (ESCI).


  • Michael, A. Koschmider, F. Mannhardt, N. Baracaldo, B. Rumpe: User-Centered and Privacy-Driven Process Mining System Design. In: 31st International Conference on Advanced Information Systems Engineering (CAiSE) Forum, vol. 350 of LNBIP, Springer, pp. 194-206, 2019
  • Baracaldo, B. Chen, H. Ludwig, J. Amir Safavi, R. Zhang: Detecting Poisoning Attacks on Machine Learning in IoT Environments. 2018 IEEE International Congress on Internet of Things, IEEE Computer Society, pp. 57-64, 2018
  • Mannhardt, A. Koschmider, N. Baracaldo, M. Weidlich, J. Michael: Privacy-preserving Process Mining: Differential Privacy for Event Logs. In: Business & Information Systems Engineering (BISE), 2019, to appear