# Asymptotically optimal private estimation under mean square loss

arXiv: https://arxiv.org/abs/1708.00059 · HTML: https://mlsanity.com/paper/1708.00059/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Optimal Algorithms for Mean Estimation under Local Differential Privacy](https://mlsanity.com/paper/2205.02466/) (2022)

## Abstract (arXiv)

We consider the minimax estimation problem of a discrete distribution with support size k under locally differential privacy constraints. A privatization scheme is applied to each raw sample independently, and we need to estimate the distribution of the raw samples from the privatized samples. A positive number ε measures the privacy level of a privatization scheme. In our previous work (arXiv:1702.00610), we proposed a family of new privatization schemes and the corresponding estimator. We also proved that our scheme and estimator are order optimal in the regime eε ll k under both ℓ22 and ℓ1 loss. In other words, for a large number of samples the worst-case estimation loss of our scheme was shown to differ from the optimal value by at most a constant factor. In this paper, we eliminate this gap by showing asymptotic optimality of the proposed scheme and estimator under the ℓ22 (mean square) loss. More precisely, we show that for any k and ε, the ratio between the worst-case estimation loss of our scheme and the optimal value approaches 1 as the number of samples tends to infinity.