Practical considerations on using private sampling for synthetic data
CoRR(2023)
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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