25 citations · 58 across the 7 of their papers we have counts for
7 papers
Evaluating the Fairness Impact of Differentially Private Synthetic Data
Blake Bullwinkel, Kristen Grabarz, Lily Ke +3
Differentially private (DP) synthetic data is a promising approach to maximizing the utility of data containing sensitive information. Due to the suppression of underrepresented cl…
Spending Privacy Budget Fairly and Wisely
Lucas Rosenblatt, Joshua Allen, Julia Stoyanovich
Differentially private (DP) synthetic data generation is a practical method for improving access to data as a means to encourage productive partnerships. One issue inherent to DP i…
U.S. Broadband Coverage Data Set: A Differentially Private Data Release
Mayana Pereira, Allen Kim, Joshua Allen +3
Broadband connectivity is a key metric in today's economy. In an era of rapid expansion of the digital economy, it directly impacts GDP. Furthermore, with the COVID-19 guidelines o…
Differentially Private Synthetic Data: Applied Evaluations and Enhancements
Lucas Rosenblatt, Xiaoyan Liu, Samira Pouyanfar +3
Machine learning practitioners frequently seek to leverage the most informative available data, without violating the data owner's privacy, when building predictive models. Differe…
Distributed Differentially Private Mutual Information Ranking and Its Applications
Ankit Srivastava, Samira Pouyanfar, Joshua Allen +2
Computation of Mutual Information (MI) helps understand the amount of information shared between a pair of random variables. Automated feature selection techniques based on MI rank…
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan Kulkarni +3
Differential privacy has emerged as the main definition for private data analysis and machine learning. The {\em global} model of differential privacy, which assumes that users tru…
Comparing Population Means under Local Differential Privacy: with Significance and Power
Bolin Ding, Harsha Nori, Paul Li +1
A statistical hypothesis test determines whether a hypothesis should be rejected based on samples from populations. In particular, randomized controlled experiments (or A/B testing…