Fidelity-agnostic synthetic data generation improves utility while retaining privacy

Patterns, vol. 6, pp. 101287

Abstract

Synthetic data are a popular method to publish useful datasets in a privacy-aware manner, making them useful across a range of scientific domains involving human subjects. They are typically generated by sampling from algorithms that mimic the probability distribution of real datasets, thereby maximizing statistical similarity to real data. However, we argue and demonstrate that synthetic data need to be similar only in ways relevant to their intended use and may neglect any irrelevant information, which in turn may improve privacy protection. As such, we propose a data synthesis method entitled fidelity-agnostic synthetic data. The method first extracts features relevant to the dataset's intended use using a neural net and then generates synthetic versions of the extracted features, after which they are decoded to mimic the real dataset. We show that our synthetic data improve performance in prediction tasks while retaining privacy protection compared to other state-of-the-art methods.

Authors 3

1. Jim L. Achterberg corresponding Aachen

   Leiden University Medical Center

   Affiliation as printed

   Public Health and Primary Care (Health Campus The Hague), Leiden University Medical Center, Leiden, South-Holland, the Netherlands

2. M. Haas Aachen

   Leiden University Medical Center

   Affiliation as printed

   Public Health and Primary Care (Health Campus The Hague), Leiden University Medical Center, Leiden, South-Holland, the Netherlands

3. Marco René Spruit Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, Leiden, South-Holland, the Netherlands

   Public Health and Primary Care (Health Campus The Hague), Leiden University Medical Center, Leiden, South-Holland, the Netherlands