Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics

15 April 2020

Papers citing "Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics"

11 / 11 papers shown

Title
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale Ryan M. Rogers S. Subramaniam Sean Peng D. Durfee Seunghyun Lee Santosh Kumar Kancha Shraddha Sahay P. Ahammad 35 79 0 14 Feb 2020
Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity Jinshuo Dong D. Durfee Ryan M. Rogers 33 11 0 30 Sep 2019
Practical Differentially Private Top- $k$ Selection with Pay-what-you-get Composition D. Durfee Ryan M. Rogers 50 87 0 10 May 2019
Privacy and Utility Tradeoff in Approximate Differential Privacy Quan Geng Wei Ding Ruiqi Guo Sanjiv Kumar 54 23 0 01 Oct 2018
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising Borja Balle Yu Wang MLT 66 403 0 16 May 2018
Renyi Differential Privacy Ilya Mironov 77 1,256 0 24 Feb 2017
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 191 6,109 0 01 Jul 2016
Privacy Odometers and Filters: Pay-as-you-Go Composition Ryan M. Rogers Aaron Roth Jonathan R. Ullman Salil P. Vadhan 48 109 0 26 May 2016
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds Mark Bun Thomas Steinke 84 831 0 06 May 2016
Concentrated Differential Privacy Cynthia Dwork G. Rothblum 68 448 0 06 Mar 2016
The Composition Theorem for Differential Privacy Peter Kairouz Sewoong Oh Pramod Viswanath 107 680 0 04 Nov 2013