23 citations · 47 across the 43 of their papers we have counts for
8 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…
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…
Private Isotonic Regression
Badih Ghazi, Pritish Kamath, Ravi Kumar +1
In this paper, we consider the problem of differentially private (DP) algorithms for isotonic regression. For the most general problem of isotonic regression over a partially order…
Faster Privacy Accounting via Evolving Discretization
Badih Ghazi, Pritish Kamath, Ravi Kumar +1
We introduce a new algorithm for numerical composition of privacy random variables, useful for computing the accurate differential privacy parameters for composition of mechanisms.…