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Found 4 papers, 1 papers with code
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees
Then, we exhibit a polynomial-time approximation algorithm with $O(|V|^{2. 5}/ \epsilon)$-additive error, and an exponential-time algorithm that meets the lower bound.
Privacy Amplification Via Bernoulli Sampling
Balancing privacy and accuracy is a major challenge in designing differentially private machine learning algorithms.
Online $k$-means Clustering on Arbitrary Data Streams
We propose a data parameter, $\Lambda(X)$, such that for any algorithm maintaining $O(k\text{poly}(\log n))$ centers at time $n$, there exists a data stream $X$ for which a loss of $\Omega(\Lambda(X))$ is inevitable.
Capacity Bounded Differential Privacy
Differential privacy, a notion of algorithmic stability, is a gold standard for measuring the additional risk an algorithm's output poses to the privacy of a single record in the dataset.
