3 papers
cs.LG2026
PE-means: Improved Differentially Private -means Clustering through Private Evolution
Thomas Humphries, Zinan Lin, Sergey Yekhanin
We study the problem of differentially private (DP) -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…
cs.CR2025
FastLloyd: Federated, Accurate, Secure, and Tunable -Means Clustering with Differential Privacy
Abdulrahman Diaa, Thomas Humphries, Florian Kerschbaum
We study the problem of privacy-preserving -means clustering in the horizontally federated setting. Existing federated approaches using secure computation suffer from substantia…