Quantifying Privacy Risks in Synthetic Data: A Study on Black-Box Membership Inference

Zenodo (CERN European Organization for Nuclear Research) 2026 Open access

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Authors: Giacomo Fantino, Marco Rondina, Antonio Vetrò, Juan Carlos De Martin
DOI: 10.5281/zenodo.18173782
Cited by: 0

Abstract

Code and data accompanying the paper: Giacomo Fantino, Marco Rondina, Antonio Vetrò, and Juan Carlos De Martin. 2026. Quantifying Privacy Risks in Synthetic Data: A Study on Black-Box Membership Inference. In Proceedings of the International Conference on Fundamental Approaches to Software Engineering (FASE 2026). Full usage instructions are available at:https://github.com/giacomofantino/Synthetic-Data-Privacy

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
7 January 2026