25 citations · 58 across the 7 of their papers we have counts for
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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…
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…