Open Source

I have (co-)implemented and released two Python packages for (Differentially Private) synthetic data generation. Both are beginner friendly and have been used in academia (UCL, Imperial College London, The Alan Turing Institute) and industry (HSBC, Hazy, Oblivious) settings:

• py-synthpop – Python implementation of the R package synthpop
• dpart – Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation (published at TPDP 2022)

I maintain a public repo containing a collection of Differentially Private (tabular) Generative Models Papers with Code. Please contribute!

I also regularly contribute to open source projects, mainly related to privacy-preserving machine learning.