Publikationen AS: Bibliographie 2025 BibTeX
@inproceedings {INPROC-2025-03,
author = {Andrea Fieschi and Pascal Hirmer and Christoph Stach},
title = {{Discovering Suitable Anonymization Techniques: A Privacy Toolbox for Data Experts}},
booktitle = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2025)},
editor = {Meike Klettke and Ralf Schenkel and Andreas Heinrich and Daniela Nicklas and Maximilian E. Sch{\"u}le and Klaus Meyer-Wegener},
address = {Bonn},
publisher = {Gesellschaft f{\"u}r Informatik},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
series = {Lecture Notes in Informatics},
volume = {P361},
pages = {827--833},
type = {Demonstration},
month = {M{\"a}rz},
year = {2025},
issn = {2944-7682},
doi = {10.18420/BTW2025-48},
keywords = {Anonymization; Privacy-Enhancing Techniques; Anonymization by Design},
language = {Englisch},
cr-category = {K.4.1 Computers and Society Public Policy Issues},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Identifying the appropriate anonymization technique is a critical yet
challenging task for developers, data scientists, and security practitioners.
Our interactive toolbox addresses this challenge by providing a comprehensive
overview of available anonymization techniques to assist privacy-conscious
developers in selecting the right one for their specific use cases. The toolbox
offers a hierarchical and classified overview of techniques, each detailed with
meta-model information. It employs a modular approach, allowing techniques to
be implemented and deployed independently. Additionally, it enables developers
to evaluate these techniques on test datasets. Our toolbox allows for the easy
addition of new categories and modules. This paper demonstrates the
anonymization toolbox{\^a}€™s capabilities, simplifying the decision-making process
in the Anonymization by Design cycle by ensuring overview, modularity, and
flexibility.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2025-03&engl=0}
}
@inproceedings {INPROC-2025-02,
author = {Andrea Fieschi and Pascal Hirmer and Christoph Stach and Bernhard Mitschang},
title = {{Characterising and Categorising Anonymization Techniques: A Literature-Based Approach}},
booktitle = {Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1 (ICISSP 2025)},
editor = {Roberto Di Pietro and Karen Renaud and Paolo Mori},
publisher = {SciTePress},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {107--118},
type = {Konferenz-Beitrag},
month = {Februar},
year = {2025},
isbn = {978-989-758-735-1},
issn = {2184-4356},
doi = {10.5220/0013379100003899},
keywords = {Privacy Protection; PRISMA Systematic Literature Research; Privacy-Enhancing Techniques; Anonymization Techniques},
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 = {Anonymization plays a crucial role in protecting personal data and ensuring
information security. However, selecting the appropriate anonymization
technique is a challenging task for developers, data scientists, and security
practitioners due to the vast array of techniques available in both research
and practice. This paper aims to assist users by offering a method for
structuring a framework that helps them make informed decisions about the most
appropriate anonymization techniques for their specific use cases. To achieve
this, we first conduct a systematic literature review following the PRISMA
guidelines to capture the current state of the art in anonymization techniques.
Based on the findings from this review, we propose a conceptual organisation of
anonymization techniques, designed to help users navigate the complex landscape
of anonymization and choose techniques that align with their security
requirements.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2025-02&engl=0}
}
@inproceedings {INPROC-2025-01,
author = {Laura Schuiki and Christoph Stach and Corinna Giebler and Eva Hoos and Bernhard Mitschang},
title = {{Enabling Trusted Data Sharing in Data Spaces: PROTON - A Privacy-by-Design Approach to Data Products}},
booktitle = {Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1 (ICISSP 2025)},
editor = {Roberto Di Pietro and Karen Renaud and Paolo Mori},
publisher = {SciTePress},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {95--106},
type = {Konferenz-Beitrag},
month = {Februar},
year = {2025},
isbn = {978-989-758-735-1},
issn = {2184-4356},
doi = {10.5220/0013372900003899},
keywords = {Distributed Data Management; Data Product; Privacy},
language = {Englisch},
cr-category = {E.1 Data Structures,
K.4.1 Computers and Society Public Policy Issues},
contact = {Senden Sie eine E-Mail an \<laura-sophie.schuiki@ipvs.uni-stuttgart.de\>.},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {In the current era of data-driven innovation, the value of data can be
significantly enhanced by facilitating its dissemination. In this context, the
data mesh concept has gained popularity in recent years. Data Mesh includes
domain experts who design so-called data products. It is imperative that all
parties involved have trust in these data products. This applies in particular
to data subjects who share their data, data owners who create the data
products, and data consumers who use them. To establish such trust, privacy
approaches are key. Due to the decentralized and distributed nature of data
mesh, however, traditional privacy strategies cannot be applied. To address
this issue, we present PROTON, a concept that facilitates the handling of
PRivacy-cOmpliant daTa prOducts by desigN. PROTON is based on three pillars: a
comprehensive description model for privacy requirements, an extended creation
process that adheres to these requirements when compiling data products, and a
refined access process for verifying compliance prior to data sharing. The
practical applicability of PROTON is illustrated by means of a real-world
application scenario that has been devised in collaboration with domain experts
from our industry partner.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2025-01&engl=0}
}
