Thomas Humphries

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20202026

most citedCache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration

2 citations · 5 across the 8 of their papers we have counts for

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cs.LG2026

PE-means: Improved Differentially Private $k$ -means Clustering through Private Evolution

Thomas Humphries, Zinan Lin, Sergey Yekhanin

We study the problem of differentially private (DP) $k$ -means clustering in Euclidean space. Previous solutions rely on summing the private data directly, which induces a sensitivi…

cs.CR2026

Interpreting the Error of Differentially Private Median Queries through Randomization Intervals

Thomas Humphries, Tim Li, Shufan Zhang +2

It can be difficult for practitioners to interpret the quality of differentially private (DP) statistics due to the added noise. One method to help analysts understand the amount o…

PE-means: Improved Differentially Private k-means Clustering through Private Evolution

Interpreting the Error of Differentially Private Median Queries through Randomization Intervals

PE-means: Improved Differentially Private $k$ -means Clustering through Private Evolution