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Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data
To the best of our knowledge, our work is the first that: (i) proposes a training and evaluation framework that does not assume that real data is available for testing the utility and fairness of machine learning models trained on synthetic data; (ii) presents the most extensive analysis of synthetic data set generation algorithms in terms of utility and fairness when used for training machine learning models; and (iii) encompasses several different definitions of fairness.
Secure Multiparty Computation for Synthetic Data Generation from Distributed Data
Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education.
An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises
Diferentially private (DP) synthetic datasets are a powerful approach for training machine learning models while respecting the privacy of individual data providers.
Metadata-Based Detection of Child Sexual Abuse Material
With such restrictions in place, the development of CSAM machine learning detection systems based on file metadata uncovers several opportunities.
