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Differentially Private Clustering in Data Streams
In this work, we provide the first differentially private streaming algorithms for $k$-means and $k$-median clustering of $d$-dimensional Euclidean data points over a stream with length at most $T$ using $poly(k, d,\log(T))$ space to achieve a constant multiplicative error and a $poly(k, d,\log(T))$ additive error.
Differentially-Private Sublinear-Time Clustering
Clustering is an essential primitive in unsupervised machine learning.
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