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When applying differential privacy to sensitive data, we can often improveperformance using external information such as other sensitive data, publicdata, or human priors. We propose to use the learning-augmented algorithms (oralgorithms with predictions) framework -- previously applied largely to improvetime complexity or competitive ratios -- as a powerful way of designing andanalyzing privacy-preserving methods that can take advantage of such externalinformation to improve utility. This idea is instantiated on the important taskof multiple quantile release, for which we derive error guarantees that scalewith a natural measure of prediction quality while (almost) recoveringstate-of-the-art prediction-independent guarantees. Our analysis enjoys severaladvantages, including minimal assumptions about the data, a natural way ofadding 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 thatlearning predictions across one or more instances can lead to large errorreductions while preserving privacy.

Learning-Augmented Private Algorithms for Multiple Quantile Release