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.

翻译：不同隐私下的平均估计是一个根本问题,但当全球敏感度非常大时,最坏情况的最佳机制在实践中并不能提供有意义的实用保障,相反,提出了各种方法,以减少与最坏情况不同的真实世界数据错误。本文采取原则性办法,产生一个在强烈意义上最优化实例的机制。除了其理论上的最佳性外,这一机制还简单而实用,并适应各种数据特点,而不需要参数调整。它很容易扩展到本地和洗涤模型。