VERANDA

Trustworthy Anonymization of Sensitive Patient Records for Remote Consultation
© 2024 Hannes Gieseler, all rights reserved
The primary aim of VERANDA is to explore and promote informational self-determination in the context of personal and sensitive medical data, especially among stigmatized groups. This includes advancing essential medical diagnostics, such as cancer detection and clinical-psychological care, which has seen increased demand during the COVID-19 pandemic. The project is closely tied to the provision of personal data for research purposes, highlighting the importance of effective risk-opportunity communication with patients and the public. By examining the conditions under which patients are willing to share their data during online consultations, the project seeks to develop communication strategies that enhance users' understanding of relevant data protection technologies.

Recent News

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Shaping Anonymous Support - Your Input Matters!

Many people avoid therapy or digital health services because they fear being judged or excluded due to sensitive personal characteristics. At the Berlin Institute of Health at Charité, we are addressing this challenge through the VERANDA research project which is funded by the Federal Ministry of Research, Technology and Space.

Our mission is to develop safe and anonymous access to digital health support such as psychotherapy – especially for individuals who currently avoid such services due to fear of stigmatization.

Teilnehmende gesucht!

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Das Projekt VERANDA sucht Teilnehmende für eine Befragung zu Bedürfnissen und Anforderungen potenzieller Nutzer*innen von anonymen Online-Fernbehandlungen. Ziel ist es, diese für Menschen aus stigmatisierten Gruppen zugänglicher zu machen.

Recent Publications

Wang, Q., Anikina, T., Feldhus, N., Ostermann, S., Möller, S. and Schmitt, V., 2025, January. Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem. In Proceedings of the 31st International Conference on Computational Linguistics (pp. 1150-1167).

Wang, Q., Anikina, T., Feldhus, N., Ostermann, S. and Möller, S., 2024, November. CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 1410-1422).

Flanagan, L. and Poikela, M., 2025. Attitudes of Developers Towards Privacy in Personal Health Applications. Studies in health technology and informatics, 327, pp.949-953.

Flanagan L, Poikela M. Current Applications of Stigma-Conscious Interventions for Healthcare. MEDINFO 2025—Healthcare Smart× Medicine Deep. 2025:465-9.


In Press

Wang, Q., Feldhus, N., Ostermann, S., Villa-Arenas, L.F., Möller, S. and Schmitt, V., 2025. FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation. arXiv preprint arXiv:2501.00777.

Dhaini, M., Erdogan, E., Feldhus, N. and Kasneci, G., 2025. Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods. arXiv preprint arXiv:2505.01198.

Franzreb, C., Das, A., Polzehl, T. and Möller, S., 2025. Private kNN-VC: Interpretable Anonymization of Converted Speech. arXiv preprint arXiv:2505.17584.

Wang Q, Anikina T, Feldhus N, Ostermann S, Splitt F, Li J, Tsoneva Y, Möller S, Schmitt V. Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems. arXiv preprint arXiv:2508.14982. 2025 Aug 20.

Wang Q, Nguyen VB, Feldhus N, Villa-Arenas LF, Seifert C, Möller S, Schmitt V. Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals. arXiv preprint arXiv:2505.13972. 2025 May 20.