title: Instance-optimal Mean Estimation Under Differential Privacy
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.
authors: Ziyue Huang Yuting Liang Ke Yi
hIndex: Ke Yi 39