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Machine Learning in Montpellier, Theory & Practice – Shubhankar Mohapatra (University of Waterloo)

Many critical AI applications, such as personalized health assistants and social network recommender systems, rely on learning from private data. Differential privacy has become the gold standard for training models on sensitive data, gaining widespread adoption in industry and government. This growing adoption has fueled research in differentially private learning, yet significant challenges remain in its deployment. In this talk, I will discuss several challenges that arise when integrating differential privacy into an end-to-end learning pipeline, from data collection to model training. I will particularly focus on the difficulties caused by inconsistencies in private datasets, such as typos and missing values. Correcting these errors is especially challenging when direct access to the raw data is restricted due to privacy constraints. I will present two recent works addressing this issue. First, I will show how leveraging correlations and dependencies in the data lets us privately quantify inconsistencies, helping estimate data quality and the effort needed for data repair. Second, I will examine the impact of missing values in private learning and introduce simple yet effective techniques for generating differentially private synthetic data to mitigate these effects.

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