PreFair: Privately Generating Justifiably Fair Synthetic Data

David Pujol, Amir Gilad, Ashwin Machanavajjhala

Research output: Contribution to journalConference articlepeer-review


When a database is protected by Differential Privacy (DP), its us-ability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the properties of the private data allows users to perform any operation on the synthetic data, while maintaining the privacy of the original data. Therefore, multiple works have been devoted to devising systems for DP synthetic data generation. However, such systems may preserve or even magnify properties of the data that make it unfair, rendering the synthetic data unfit for use. In this work, we present PreFair, a system that allows for DP fair synthetic data generation. PreFair extends the state-of-the-art DP data generation mechanisms by incorporating a causal fairness criterion that ensures fair synthetic data. We adapt the notion of justifiable fairness to fit the synthetic data generation scenario. We further study the problem of generating DP fair synthetic data, showing its intractability and designing algorithms that are optimal under certain assumptions. We also provide an extensive experimental evaluation, showing that PreFair generates synthetic data that is significantly fairer than the data generated by leading DP data generation mechanisms, while remaining faithful to the private data.

Original languageEnglish
Pages (from-to)1573-1586
Number of pages14
JournalProceedings of the VLDB Endowment
Issue number6
StatePublished - 2023
Externally publishedYes
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: 28 Aug 20231 Sep 2023


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