24 citations · 101 across the 38 of their papers we have counts for
11 papers · 1 filter
On Differentially Private Counting on Trees
Badih Ghazi, Pritish Kamath, Ravi Kumar +2
We study the problem of performing counting queries at different levels in hierarchical structures while preserving individuals' privacy. Motivated by applications, we propose a ne…
Regression with Label Differential Privacy
Badih Ghazi, Pritish Kamath, Ravi Kumar +4
We study the task of training regression models with the guarantee of label differential privacy (DP). Based on a global prior distribution on label values, which could be obtained…
Differentially Private Heatmaps
Badih Ghazi, Junfeng He, Kai Kohlhoff +4
We consider the task of producing heatmaps from users' aggregated data while protecting their privacy. We give a differentially private (DP) algorithm for this task and demonstrate…
Private Counting of Distinct and k-Occurring Items in Time Windows
Badih Ghazi, Ravi Kumar, Pasin Manurangsi +1
In this work, we study the task of estimating the numbers of distinct and -occurring items in a time window under the constraint of differential privacy (DP). We consider severa…
Private Ad Modeling with DP-SGD
Carson Denison, Badih Ghazi, Pritish Kamath +6
A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descent (DP-SGD). While this algorithm has been evaluated on text and image data, it h…
Anonymized Histograms in Intermediate Privacy Models
Badih Ghazi, Pritish Kamath, Ravi Kumar +1
We study the problem of privately computing the anonymized histogram (a.k.a. unattributed histogram), which is defined as the histogram without item labels. Previous works have pro…