Software Engineering Daily - Privacy Engineering with Alex Watson

Protecting your customers begins with best practices for securely capturing, storing, and protecting the data you collect for or about them.  When an organization has a large enough dataset, needs typically arise for doing analytical workloads or training machine learning models on this data.  If you use random or mock data to generate a report or train a model, you arrive at an output that doesn’t reflect the true use case of the organization.  Success on tasks like this seems to require production data.

Alternatively, perhaps production-like data is good enough.  In this episode, I interview Alex Watson, co-founder and chief product officer at gretel.  We discuss their solution for privacy preserving synthetic data that remains representative of the underlying dataset.