An Algorithmic Framework For Differentially Private Data Analysis on
Trusted Processors

v1v2 (latest)

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

2 July 2018

Janardhan Kulkarni

Sergey Yekhanin

ArXiv (abs)PDF HTML

Papers citing "An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors"

11 / 11 papers shown

Title
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation Wei-Ning Chen Danni Song Ayfer Özgür Peter Kairouz FedML 83 28 0 04 Apr 2023
Adore: Differentially Oblivious Relational Database Operators Lianke Qin Rajesh Jayaram E. Shi Zhao Song Danyang Zhuo Shumo Chu 93 14 0 10 Dec 2022
Mitigating Leakage from Data Dependent Communications in Decentralized Computing using Differential Privacy Riad Ladjel N. Anciaux A. Bellet Guillaume Scerri FedML 116 0 0 23 Dec 2021
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems Jiankai Jin Eleanor McMurtry Benjamin I. P. Rubinstein O. Ohrimenko 76 38 0 10 Dec 2021
Differentially Private Synthetic Data: Applied Evaluations and Enhancements Lucas Rosenblatt Xiao-Yang Liu Samira Pouyanfar Eduardo de Leon Anuj M. Desai Joshua Allen SyDa 74 67 0 11 Nov 2020
Distributed Differentially Private Mutual Information Ranking and Its Applications Ankit Srivastava Samira Pouyanfar Joshua Allen Ken Johnston Qida Ma 20 0 0 22 Sep 2020
Differentially private cross-silo federated learning Mikko A. Heikkilä A. Koskela Kana Shimizu Samuel Kaski Antti Honkela FedML 66 24 0 10 Jul 2020
Towards Probabilistic Verification of Machine Unlearning David M. Sommer Liwei Song Sameer Wagh Prateek Mittal AAML 119 73 0 09 Mar 2020
Improving Utility and Security of the Shuffler-based Differential Privacy Tianhao Wang Bolin Ding Min Xu Zhicong Huang Cheng Hong Jingren Zhou Ninghui Li S. Jha 151 11 0 30 Aug 2019
SoK: Differential Privacies Damien Desfontaines Balázs Pejó 163 126 0 04 Jun 2019
Contamination Attacks and Mitigation in Multi-Party Machine Learning Jamie Hayes O. Ohrimenko AAML FedML 114 75 0 08 Jan 2019