Learning-Augmented Private Algorithms for Multiple Quantile Release
This work addresses the challenge of enhancing privacy-preserving data analysis for sensitive datasets by leveraging predictions, though it is incremental as it applies an existing framework to a specific task.
The paper tackles the problem of improving utility in differentially private multiple quantile release by incorporating external information through learning-augmented algorithms, resulting in error guarantees that scale with prediction quality and large error reductions in experiments.
When applying differential privacy to sensitive data, we can often improve performance using external information such as other sensitive data, public data, or human priors. We propose to use the learning-augmented algorithms (or algorithms with predictions) framework -- previously applied largely to improve time complexity or competitive ratios -- as a powerful way of designing and analyzing privacy-preserving methods that can take advantage of such external information to improve utility. This idea is instantiated on the important task of multiple quantile release, for which we derive error guarantees that scale with a natural measure of prediction quality while (almost) recovering state-of-the-art prediction-independent guarantees. Our analysis enjoys several advantages, including minimal assumptions about the data, a natural way of adding robustness, and the provision of useful surrogate losses for two novel ``meta" algorithms that learn predictions from other (potentially sensitive) data. We conclude with experiments on challenging tasks demonstrating that learning predictions across one or more instances can lead to large error reductions while preserving privacy.