7 citations · 7 across the 1 of their papers we have counts for
3 papers
cs.LG2021
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…
cs.CR2020
Locally Differentially Private Analysis of Graph Statistics
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Differentially private analysis of graphs is widely used for releasing statistics from sensitive graphs while still preserving user privacy. Most existing algorithms however are in…
cs.LG2019★ 7 cited
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…