65 citations · 177 across the 68 of their papers we have counts for
8 papers · 1 filter
Private Rank Aggregation in Central and Local Models
Daniel Alabi, Badih Ghazi, Ravi Kumar +1
In social choice theory, (Kemeny) rank aggregation is a well-studied problem where the goal is to combine rankings from multiple voters into a single ranking on the same set of ite…
User-Level Private Learning via Correlated Sampling
Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample. In this work, we consider the setting where each user holds $…
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
Badih Ghazi, Ravi Kumar, Pasin Manurangsi +2
The shuffle model of differential privacy has attracted attention in the literature due to it being a middle ground between the well-studied central and local models. In this work,…
Large-Scale Differentially Private BERT
Rohan Anil, Badih Ghazi, Vineet Gupta +2
In this work, we study the large-scale pretraining of BERT-Large with differentially private SGD (DP-SGD). We show that combined with a careful implementation, scaling up the batch…
Google COVID-19 Vaccination Search Insights: Anonymization Process Description
Shailesh Bavadekar, Adam Boulanger, John Davis +22
This report describes the aggregation and anonymization process applied to the COVID-19 Vaccination Search Insights (published at http://goo.gle/covid19vaccinationinsights), a publ…
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Badih Ghazi, Ravi Kumar, Pasin Manurangsi +1
Differential privacy (DP) is a formal notion for quantifying the privacy loss of algorithms. Algorithms in the central model of DP achieve high accuracy but make the strongest trus…
Locally Private k-Means in One Round
Alisa Chang, Badih Ghazi, Ravi Kumar +1
We provide an approximation algorithm for k-means clustering in the one-round (aka non-interactive) local model of differential privacy (DP). This algorithm achieves an approximati…
Deep Learning with Label Differential Privacy
Badih Ghazi, Noah Golowich, Ravi Kumar +2
The Randomized Response (RR) algorithm is a classical technique to improve robustness in survey aggregation, and has been widely adopted in applications with differential privacy g…