6 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.CR2019★ 6 cited
The Value of Collaboration in Convex Machine Learning with Differential Privacy
Nan Wu, Farhad Farokhi, David Smith +1
In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differe…
cs.CR2017★ 1 cited
More Flexible Differential Privacy: The Application of Piecewise Mixture Distributions in Query Release
David B. Smith, Kanchana Thilakarathna, Mohamed Ali Kaafar
There is an increasing demand to make data "open" to third parties, as data sharing has great benefits in data-driven decision making. However, with a wide variety of sensitive dat…