1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchAlyssa Hugheshttps://www.microsoft.com/en-us/research/people/v-alyhu/Locally Private Gaussian Estimation - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="DhhWreVgfY"><a href="https://www.microsoft.com/en-us/research/publication/locally-private-gaussian-estimation/">Locally Private Gaussian Estimation</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/locally-private-gaussian-estimation/embed/#?secret=DhhWreVgfY" width="600" height="338" title="“Locally Private Gaussian Estimation” — Microsoft Research" data-secret="DhhWreVgfY" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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We study a basic private estimation problem: each of n users draws a single i.i.d. sample from an unknown Gaussian distribution, and the goal is to estimate the mean of this Gaussian distribution while satisfying local differential privacy for each user. Informally, local differential privacy requires that each data point is individually and independently privatized before it […]