35 citations · 159 across the 35 of their papers we have counts for
8 papers · 1 filter
Differential privacy and Sublinear time are incompatible sometimes
Jeremiah Blocki, Hendrik Fichtenberger, Elena Grigorescu +1
Differential privacy and sublinear algorithms are both rapidly emerging algorithmic themes in times of big data analysis. Although recent works have shown the existence of differen…
Differentially Private -Heavy Hitters in the Sliding Window Model
Jeremiah Blocki, Seunghoon Lee, Tamalika Mukherjee +1
The data management of large companies often prioritize more recent data, as a source of higher accuracy prediction than outdated data. For example, the Facebook data policy retain…
How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity
Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee +1
We develop a framework for efficiently transforming certain approximation algorithms into differentially-private variants, in a black-box manner. Specifically, our results focus on…
Privately Estimating Graph Parameters in Sublinear time
Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee
We initiate a systematic study of algorithms that are both differentially private and run in sublinear time for several problems in which the goal is to estimate natural graph para…
On Explicit Constructions of Extremely Depth Robust Graphs
Jeremiah Blocki, Mike Cinkoske, Seunghoon Lee +1
A directed acyclic graph is said to be -depth robust if for every subset of nodes the graph still contains a directed path of le…
A New Connection Between Node and Edge Depth Robust Graphs
Jeremiah Blocki, Mike Cinkoske
Given a directed acyclic graph (DAG) , we say that is -depth-robust (resp. -edge-depth-robust) if for any set (resp. ) of…