Building differentially private machine learning models using TensorFlow
Who is this presentation for?
- Data scientists, software engineers, and product developers
In recent years, the world has become increasingly data driven, and individuals and organizations have developed a stronger awareness and concern for the privacy of their sensitive data. It’s been shown that it’s impossible to disclose statistical results about a private database without revealing some information. Following research on the privacy of sensitive databases, a number of big players such as Google, Apple, and Uber have turned to differential privacy to help guarantee the privacy of sensitive data. That attention from major technology firms has helped bring differential privacy out of research labs and into the realm of software engineering and product development. Differential privacy is now something that smaller firms and software startups are adopting and finding great value in.
Apart from privacy guarantees, advances in differential privacy also allow businesses to unlock more capabilities and increase data utility. One of these capabilities includes the ability to transfer knowledge from existing data through differentially private ensemble models without data privacy concerns. As differential privacy garners recognition in large tech companies, efforts to make current state-of-the-art research more accessible to the general public and small startups are underway. As a contribution to the broader community, Georgian Partners has provided its differential privacy library to the TensorFlow community, making differentially private stochastic gradient descent available in a user-friendly and easy-to-use API that allows users to train private logistic regression and support vector machines.
Chang Liu and Ji Chao Zhang examine differential privacy and its use cases, the new component of the TensorFlow Privacy library, and real-world scenarios along with demonstrations for how to apply the tools.
- A basic understanding of basic machine learning
What you'll learn
- Understand differential privacy and the new components in the TensorFlow Privacy library
- See real-world scenarios and the practical applications of differential privacy and the TensorFlow Privacy library
- Discover where businesses can find value in differential privacy
Chang Liu is an applied research scientist at Georgian Partners and a member of the Georgian impact team, where she draws on her in-depth knowledge of mathematical and combinatorial optimization to help Georgian’s portfolio companies. Previously, Chang was a risk analyst at Manulife Bank, where she built models to assess the bank’s risk exposure based on extensive market research, including evaluating and predicting the impact of the oil price drop to the mortgage lending risks in Alberta in 2014. Chang holds a master of applied science in operations research from the University of Toronto, where she specialized in combinatorial optimization, and a bachelor’s degree in mathematics from the University of Waterloo.
Ji Chao Zhang
Ji Chao Zhang is the director of software engineering and a member of the Georgian impact team. In that role, he leads its internal software engineering efforts and supports portfolio engagements.
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