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arXiv · 2106.00463

Instance-optimal Mean Estimation Under Differential Privacy

Ziyue Huang·Yuting Liang·Ke Yi
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Abstract

Mean estimation under differential privacy is a fundamental problem, but worst-case optimal mechanisms do not offer meaningful utility guarantees in practice when the global sensitivity is very large. Instead, various heuristics have been proposed to reduce the error on real-world data that do not resemble the worst-case instance. This paper takes a principled approach, yielding a mechanism that is instance-optimal in a strong sense. In addition to its theoretical optimality, the mechanism is also simple and practical, and adapts to a variety of data characteristics without the need of parameter tuning. It easily extends to the local and shuffle model as well.

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Ziyue Huang, Yuting Liang, Ke Yi. 2021-06-01. Instance-optimal Mean Estimation Under Differential Privacy. https://arxiv.org/abs/2106.00463

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