资源分类
》 资源 》论文》Locally Private Gaussian Estimation

Locally Private Gaussian Estimation

2020-02-21 | |  66 |   43 |   0

Abstract

We study a basic private estimation problem: each of n users draws a single i.i.d. sample from an unknown Gaussian distribution 图片.png and the goal is to estimate µ while guaranteeing local differential privacy for each user. As minimizing the number of rounds of interaction is important in the local setting, we provide adaptive two-round solutions and nonadaptive one-round solutions to this problem. We match these upper bounds with an information-theoretic lower bound showing that our accuracy guarantees are tight up to logarithmic factors for all sequentially interactive locally private protocols.

上一篇：Shadowing Properties of Optimization Algorithms

下一篇：Differentiable Ranks and Sorting using Optimal Transport

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...