Aaron Roth  

Computer and Information SCience Dept.

"The Algorithmic and Game Theoretic Foundations of Data Privacy"

 

Abstract

Consider the following conundrum: You are the administrator of a large data set at a hospital (or search engine, or social network, or phone provider, or...) The data you hold is very valuable, and you would like to make it available to researchers with expertise in statistics and machine learning so that you can better make use of it. However, the data is also highly sensitive! It consists of patient medical records, and although you would like to make aggregate statistics available, you must do so in a way that does not compromise the privacy of any individual who may (or may not!) be in the data set. What can you do?

And what is the measure of a good solution? Can you provide strong enough guarantees to actually incentivize individuals to allow their data to be used in your study? How should they reason about their privacy, and their cost for its loss?

In this talk I'll introduce these questions, propose some solutions, and discuss how our current state of knowledge guides exciting directions for future work.

 

 

Refreshments will be served on the

2nd Floor Mezzazine Level

immediately following the talk.

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