19 citations · 63 across the 17 of their papers we have counts for
7 papers · 1 filter
Algorithms with More Granular Differential Privacy Guarantees
Badih Ghazi, Ravi Kumar, Pasin Manurangsi +1
Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parame…
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,…
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…
On Distributed Differential Privacy and Counting Distinct Elements
Lijie Chen, Badih Ghazi, Ravi Kumar +1
We study the setup where each of users holds an element from a discrete set, and the goal is to count the number of distinct elements across all users, under the constraint of…
Pure Differentially Private Summation from Anonymous Messages
Badih Ghazi, Noah Golowich, Ravi Kumar +3
The shuffled (aka anonymous) model has recently generated significant interest as a candidate distributed privacy framework with trust assumptions better than the central model but…