9 citations · 28 across the 11 of their papers we have counts for
4 papers · 1 filter
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees
Jacob Imola, Alessandro Epasto, Mohammad Mahdian +2
Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of differentially private approxi…
Privacy Amplification Via Bernoulli Sampling
Jacob Imola, Kamalika Chaudhuri
Balancing privacy and accuracy is a major challenge in designing differentially private machine learning algorithms. One way to improve this tradeoff for free is to leverage the no…
Online -means Clustering on Arbitrary Data Streams
Robi Bhattacharjee, Jacob Imola, Michal Moshkovitz +1
We consider online -means clustering where each new point is assigned to the nearest cluster center, after which the algorithm may update its centers. The loss incurred is the s…
Capacity Bounded Differential Privacy
Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
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 d…