Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models

TL;DR

This paper proposes 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.

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.

Figures (1)

• Figure 1: An fg encoding a full joint probability distribution for an epidemic example Hoffmann2022a. We omit the input-output pairs of the factors for brevity.

Theorems & Definitions (1)

• definition thmcounterdefinition: Factor Graph