2025
08. Mai
Towards Learning Differentially Private Probabilistic Relational Models II.
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. Building on the previous talk, we take a closer look at how to efficiently learn a PRM from a given propositional probabilistic model.
Malte Luttermann
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24. April
Towards Learning Differentially Private Probabilistic Relational Models I.
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 May
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 Bruges
14 March
DP Helmet: Distributed Non-Interactive Privacy-Preserving Learning of Convex Optimization problems
Moritz Kirschte
15 February
Anon Terminology
01.February
The Principles of the GDPR
Mr Bruegger and Mr Zwingelberg from the ULD (Independent State Center for Data Protection)
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2023
07.December
Uzl-Psychology in AnoMed
Jonas Obleser
09.November
Basics of clinical studies: current concepts, challenges and opportunities
Jens Fiehler, CEO of eppdata
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26 October
S-GBDT: Differentially Private Training of Gradient Boosting Decision Trees
Thorsten Peinemann and Moritz Kirschte
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06.July
Gaussian Processes and Differential Privacy
English 
German