Designing Differentially Private Estimators in High Dimensions

Understand

We study differentially private mean estimation in a high-dimensional setting.

• Existing differential privacy techniques applied to large dimensions lead to computationally intractable problems or estimators with excessive privacy loss.
• Recent work in high-dimensional robust statistics has identified computationally tractable mean estimation algorithms with asymptotic dimension-independent error guarantees.
• We incorporate these results to develop a strict bound on the global sensitivity of the robust mean estimator.