APEx: Accuracy-Aware Differentially Private Data Exploration

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

Fair Decision Making using Privacy-Protected Data

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

Principled Evaluation of Differentially Private Algorithms using DPBench

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

Differentially Private Algorithms for Empirical Machine Learning

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.

BIG-bench Machine Learning General Classification

Capacity Bounded Differential Privacy

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.