41 citations · 55 across the 6 of their papers we have counts for
6 papers
Improving the Utility of Differentially Private Clustering through Dynamical Processing
Junyoung Byun, Yujin Choi, Jaewook Lee
This study aims to alleviate the trade-off between utility and privacy of differentially private clustering. Existing works focus on simple methods, which show poor performance for…
Renyi Differentially Private ADMM for Non-Smooth Regularized Optimization
Chen Chen, Jaewoo Lee
In this paper we consider the problem of minimizing composite objective functions consisting of a convex differentiable loss function plus a non-smooth regularization term, such as…
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget
Jaewoo Lee, Daniel Kifer
Iterative algorithms, like gradient descent, are common tools for solving a variety of problems, such as model fitting. For this reason, there is interest in creating differentiall…
Differentially Private Confidence Intervals for Empirical Risk Minimization
Yue Wang, Daniel Kifer, Jaewoo Lee
The process of data mining with differential privacy produces results that are affected by two types of noise: sampling noise due to data collection and privacy noise that is desig…
Postprocessing for Iterative Differentially Private Algorithms
Jaewoo Lee, Daniel Kifer
Iterative algorithms for differential privacy run for a fixed number of iterations, where each iteration learns some information from data and produces an intermediate output. Howe…
Revisiting Differentially Private Hypothesis Tests for Categorical Data
Yue Wang, Jaewoo Lee, Daniel Kifer
In this paper, we consider methods for performing hypothesis tests on data protected by a statistical disclosure control technology known as differential privacy. Previous approach…