Practical considerations on using private sampling for synthetic data

• URL: http://arxiv.org/abs/2312.07139v1
• Date: Tue, 12 Dec 2023 10:20:04 GMT
• Title: Practical considerations on using private sampling for synthetic data
• Authors: Clément Pierquin, Bastien Zimmermann, Matthieu Boussard,
• Abstract summary: Differential privacy for synthetic data generation has received much attention due to the ability of preserving privacy while freely using the synthetic data. Private sampling is the first noise-free method to construct differentially private synthetic data with rigorous bounds for privacy and accuracy. We provide an implementation of the private sampling algorithm and discuss the realism of its constraints in practical cases.
• Score: 1.3654846342364308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence and data access are already mainstream. One of the main challenges when designing an artificial intelligence or disclosing content from a database is preserving the privacy of individuals who participate in the process. Differential privacy for synthetic data generation has received much attention due to the ability of preserving privacy while freely using the synthetic data. Private sampling is the first noise-free method to construct differentially private synthetic data with rigorous bounds for privacy and accuracy. However, this synthetic data generation method comes with constraints which seem unrealistic and not applicable for real-world datasets. In this paper, we provide an implementation of the private sampling algorithm and discuss the realism of its constraints in practical cases.

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