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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