3 citations · 3 across the 3 of their papers we have counts for
5 papers · 1 filter
Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity
Jinshuo Dong, David Durfee, Ryan Rogers
Composition is one of the most important properties of differential privacy (DP), as it allows algorithm designers to build complex private algorithms from DP primitives. We consid…
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
David Durfee, Laxman Dhulipala, Janardhan Kulkarni +3
In this paper we study the problem of dynamically maintaining graph properties under batches of edge insertions and deletions in the massively parallel model of computation. In thi…
Fully Dynamic Spectral Vertex Sparsifiers and Applications
David Durfee, Yu Gao, Gramoz Goranci +1
We study \emph{dynamic} algorithms for maintaining spectral vertex sparsifiers of graphs with respect to a set of terminals of our choice. Such objects preserve pairwise resist…
Efficient Second-Order Shape-Constrained Function Fitting
David Durfee, Yu Gao, Anup B. Rao +1
We give an algorithm to compute a one-dimensional shape-constrained function that best fits given data in weighted- norm. We give a single algorithm that works for a va…
Practical Differentially Private Top- Selection with Pay-what-you-get Composition
David Durfee, Ryan Rogers
We study the problem of top- selection over a large domain universe subject to user-level differential privacy. Typically, the exponential mechanism or report noisy max are the…