On Distributed Differential Privacy and Counting Distinct Elements

21 September 2020

Papers citing "On Distributed Differential Privacy and Counting Distinct Elements"

9 / 9 papers shown

Title
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 48 0 0 21 Feb 2025
Adore: Differentially Oblivious Relational Database Operators Lianke Qin Rajesh Jayaram E. Shi Zhao-quan Song Danyang Zhuo Shumo Chu 30 14 0 10 Dec 2022
Privacy Amplification via Shuffling for Linear Contextual Bandits Evrard Garcelon Kamalika Chaudhuri Vianney Perchet Matteo Pirotta FedML 24 18 0 11 Dec 2021
Tight Bounds for Differentially Private Anonymized Histograms Pasin Manurangsi PICV 19 6 0 05 Nov 2021
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh Amer Sinha FedML 59 36 0 27 Sep 2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh FedML 24 48 0 08 Jun 2021
Private Aggregation from Fewer Anonymous Messages Badih Ghazi Pasin Manurangsi Rasmus Pagh A. Velingker FedML 39 55 0 24 Sep 2019
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 141 420 0 29 Nov 2018
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 88 278 0 02 Oct 2017