Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing

• URL: http://arxiv.org/abs/2306.10308v2
• Date: Thu, 21 Sep 2023 09:17:16 GMT
• Title: Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing
• Authors: Matthieu Meeus, Florent Gu\'epin, Ana-Maria Cretu and Yves-Alexandre de Montjoye
• Abstract summary: We propose a principled vulnerable record identification technique for synthetic data publishing. We show it to strongly outperform previous ad-hoc methods across datasets and generators. We show it to accurately identify vulnerable records when synthetic data generators are made differentially private.
• Score: 9.061271587514215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic data is seen as the most promising solution to share individual-level data while preserving privacy. Shadow modeling-based Membership Inference Attacks (MIAs) have become the standard approach to evaluate the privacy risk of synthetic data. While very effective, they require a large number of datasets to be created and models trained to evaluate the risk posed by a single record. The privacy risk of a dataset is thus currently evaluated by running MIAs on a handful of records selected using ad-hoc methods. We here propose what is, to the best of our knowledge, the first principled vulnerable record identification technique for synthetic data publishing, leveraging the distance to a record's closest neighbors. We show our method to strongly outperform previous ad-hoc methods across datasets and generators. We also show evidence of our method to be robust to the choice of MIA and to specific choice of parameters. Finally, we show it to accurately identify vulnerable records when synthetic data generators are made differentially private. The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective. We here propose a simple yet highly effective method to do so. We hope our method will enable practitioners to better estimate the risk posed by synthetic data publishing and researchers to fairly compare ever improving MIAs on synthetic data.

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