Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models

• URL: http://arxiv.org/abs/2409.04194v2
• Date: Wed, 2 Oct 2024 17:01:58 GMT
• Title: Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models
• Authors: Malte Luttermann, Ralf Möller, Mattis Hartwig,
• Abstract summary: Probabilistic relational models provide a well-established formalism to combine first-order logic and probabilistic models. The field of artificial intelligence requires increasingly large amounts of relational training data for various machine learning tasks. Collecting real-world data is often challenging due to privacy concerns, data protection regulations, high costs, and so on.
• Score: 3.877001015064152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Probabilistic relational models provide a well-established formalism to combine first-order logic and probabilistic models, thereby allowing to represent relationships between objects in a relational domain. At the same time, the field of artificial intelligence requires increasingly large amounts of relational training data for various machine learning tasks. Collecting real-world data, however, is often challenging due to privacy concerns, data protection regulations, high costs, and so on. To mitigate these challenges, the generation of synthetic data is a promising approach. In this paper, we solve the problem of generating synthetic relational data via probabilistic relational models. In particular, we propose a fully-fledged pipeline to go from relational database to probabilistic relational model, which can then be used to sample new synthetic relational data points from its underlying probability distribution. As part of our proposed pipeline, we introduce a learning algorithm to construct a probabilistic relational model from a given relational database.

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