2025
24. April
Learning Differently Private Probabilistic Relational Models
Probabilistic relational models (PRMs) provide a well-established formalism to combine first-order logic and probabilistic models. By reasoning over groups of indistinguishable objects, PRMs abstract from individuals and thus are a promising formalism to generate synthetic relational data that can be made publicly available without violating the privacy of individuals. We investigate how a PRM can be learned from a given propositional probabilistic model and outline the use case of relational data synthesis using PRMs.
Malte Luttermann
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2024
21. Juni
A Tale of Fully-Homomorphic Encryption and its Applications in Healthcare
Saleh Mulhem
23.Mai
Optimizing for Statistical Independence using a KNN Density Estimator
Kathleen Anderson
11.April
Feature extraction as a primer for privacy-preserving medical data analysis: Example approaches for facial video data
Nele Brügge
14.März
DP Helmet: Distributed Non-Interactive Privacy-Preserving Learning of Convex Optimization problems
Moritz Kirschte
15.Februar
Anon Terminology
01.Februar
The Principles of the GDPR (Die Prinzipien der DS-GVO)
Herr Bruegger und Herr Zwingelberg vom ULD (Unabhängiges Landeszentrum für Datenschutz)
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2023
07.Dezember
Uzl-Psychology in AnoMed
Jonas Obleser
09.November
Grundlagen Clinical studies: currenc concepts, callenges and opportunities
Jens Fiehler, CEO of eppdata
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26.Oktober
S-GBDT: Differentially Private Training of Gradient Boosting Decision Trees
Thorsten Peinemann und Moritz Kirschte
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06.Juli
Gaussian Processes and Differential Privacy