@article{hanisch25anonymization,

title = {Anonymization Techniques for Behavioral Biometric Data: A Survey},

author = {Simon Hanisch and Patricia Arias-Cabarcos and Javier Parra-Arnau and Thorsten Strufe},

url = {https://doi.org/10.1145/3729418},

doi = {10.1145/3729418},

issn = {0360-0300},

year  = {2025},

date = {2025-04-01},

journal = {ACM Comput. Surv.},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schaffland_Nelson_Schöning_2024,

title = {Simulating Traffic Networks: Driving SUMO Towards Digital Twins},

author = {Axel Schaffland and Jonas Nelson and Julius Schöning},

url = {https://www.tib-op.org/ojs/index.php/scp/article/view/1105},

doi = {10.52825/scp.v5i.1105},

year  = {2024},

date = {2024-07-01},

journal = {SUMO Conference Proceedings},

volume = {5},

pages = {113–125},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gilbert2024,

title = {Augmented non-hallucinating large language models as medical information curators},

author = {Stephen Gilbert and Jakob Nikolas Kather and Aidan Hogan},

url = {https://doi.org/10.1038/s41746-024-01081-0},

year  = {2024},

date = {2024-04-01},

journal = {npj Digital Medicine},

volume = {7},

number = {100},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gehrke2024,

title = {Lifting in Support of Privacy-Preserving Probabilistic Inference},

author = {Marcel Gehrke and Johannes Liebenow and Esfandiar Mohammadi and Tanya Braun},

url = {https://link.springer.com/article/10.1007/s13218-024-00851-y},

year  = {2024},

date = {2024-01-01},

journal = {German Journal of Artificial Intelligence},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Derraz2024,

title = {New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology},

author = {Bouchra Derraz and Gabriele Breda and Christoph Kaempf and Franziska Baenke and Fabienne Cotte and Kristin Reiche and Ulrike Köhl and Jakob Nikolas Kather and Deborah Eskenazy and Stephen Gilbert},

url = {https://doi.org/10.1038/s41698-024-00517-w},

year  = {2024},

date = {2024-01-01},

journal = {npj Precision Oncology},

volume = {8},

number = {23},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gilbert2024b,

title = {Citizen data sovereignty is key to wearables and wellness data reuse for the common good},

author = {Stephen Gilbert and Katie Baca-Motes and Giorgio Quer and Marc Wiedermann and Dirk Brockmann},

url = {https://doi.org/10.1038/s41746-024-01004-z},

year  = {2024},

date = {2024-01-01},

journal = {npj Digital Medicine},

volume = {7},

number = {27},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info15010005,

title = {A Scoping Review on Analysis of the Barriers and Support Factors of Open Data},

author = {Norbert Lichtenauer and Lukas Schmidbauer and Sebastian Wilhelm and Florian Wahl},

url = {https://www.mdpi.com/2078-2489/15/1/5},

doi = {10.3390/info15010005},

issn = {2078-2489},

year  = {2024},

date = {2024-01-01},

journal = {Information},

volume = {15},

number = {1},

abstract = {Background: Using personal data as Open Data is a pervasive topic globally, spanning various sectors and disciplines. Recent technological advancements, particularly in artificial intelligence and algorithm-driven analysis, have significantly expanded the capacity for the automated analysis of vast datasets. There’s an expectation that Open Data analysis can drive innovation, enhance services, and streamline administrative processes. However, this necessitates a legally and ethically sound framework alongside intelligent technical tools to comprehensively analyze data for societal benefit. Methodology: A systematic review across seven databases (MEDLINE, CINAHL, BASE, LIVIVO, Web of Science, IEEExplore, and ACM) was conducted to assess the current research on barriers, support factors, and options for the anonymized processing of personal data as Open Data. Additionally, a supplementary search was performed in Google Scholar. A total of n=1192 studies were identified, and n=55 met the inclusion criteria through a multi-stage selection process for further analysis. Results: Fourteen potential supporting factors (n=14) and thirteen barriers (n=13) to the provision and anonymization of personal data were identified. These encompassed technical prerequisites as well as institutional, personnel, ethical, and legal considerations. These findings offer insights into existing obstacles and supportive structures within Open Data processes for effective implementation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{miranda24overview,

title = {An overview of proposals towards the privacy-preserving publication of trajectory data},

author = {Àlex Miranda-Pascual and Patricia Guerra-Balboa and Javier Parra-Arnau and Jordi Forné and Thorsten Strufe},

year  = {2024},

date = {2024-01-01},

journal = {International Journal of Information Security},

pages = {1–37},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hanisch24false,

title = {A False Sense of Privacy: Towards a Reliable Evaluation Methodology for the Anonymization of Biometric Data},

author = {Simon Hanisch and Julian Todt and Jose Patino and Nicholas Evans and Thorsten Strufe},

year  = {2024},

date = {2024-01-01},

journal = {Proceedings on Privacy Enhancing Technologies},

volume = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}