no code implementations • NeurIPS 2019 • Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
Differential privacy, a notion of algorithmic stability, is a gold standard for measuring the additional risk an algorithm's output poses to the privacy of a single record in the dataset.
1 code implementation • 29 May 2019 • Satya Kuppam, Ryan McKenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Data collected about individuals is regularly used to make decisions that impact those same individuals.
Databases
1 code implementation • 29 Dec 2017 • Chang Ge, Xi He, Ihab F. Ilyas, Ashwin Machanavajjhala
Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets.
Databases
1 code implementation • 15 Dec 2015 • Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang
Differential privacy has become the dominant standard in the research community for strong privacy protection.
Databases Cryptography and Security
no code implementations • 20 Nov 2014 • Ben Stoddard, Yan Chen, Ashwin Machanavajjhala
In this paper, we develop differentially private algorithms that mirror real world empirical machine learning workflows.