UPDATED: Video Added from the Conference!
Introducing Catherine Nelson > @DrCatNelson < presenting “Practical Privacy in Machine Learning Systems”.
Catherine Nelson is a Senior Data Scientist for Concur Labs at SAP Concur, where she explores innovative ways to use machine learning to improve the experience of a business traveller. She is particularly interested in privacy-preserving ML and applying deep learning to enterprise data. In her previous career as a geophysicist she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University.
Data privacy is a huge topic right now for any business using personal data. People are questioning whether they should allow companies to collect data about them, and they are asking about what happens to that data after they hand it over. Machine learning systems often depend on data collected from their users to make accurate predictions. But can we build cool products powered by machine learning while still providing privacy for our users?
To start with, privacy is not a binary choice. There are many options available to developers for adding privacy to ML. In this talk, I’ll discuss some practical options, from simple methods to differential privacy, and show some examples of how we can incorporate these into real products. I’ll show how we’ve built a Data Washing Machine that allows us to provide nuanced levels of privacy via a simple API. I’ll also explore the tradeoff between user privacy and model accuracy, and show how we can still make accurate predictions with our models even when privacy is increased.
Come check out Catherine Nelson’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!
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