<ul><li>Ratio statistics, such as relative risk and odds ratios, are crucial in various machine learning applications but have been overlooked in the context of differential privacy.</li><li>A recent paper aims to address this gap by offering insights into evaluating ratio statistics while preserving differential privacy.</li><li>The study shows that a simple algorithm can deliver strong privacy protection, sample accuracy, and mitigate bias issues, even with small sample sizes.</li><li>The research also introduces a differentially private estimator for relative risk, demonstrates its consistency, and presents a method for constructing valid confidence intervals, thereby enhancing ratio estimation in private machine learning processes.</li></ul>

Differentially private ratio statistics

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