Seminars & Colloquia
"Protecting Privacy and Guaranteeing Generalization with Algorithmic Stability"
Monday February 05, 2018 09:30 AM
Location: 3211, EB2 NCSU Centennial Campus
(Visitor parking instructions)
In this talk, I will illustrate how privacy can be unwittingly compromised -- i.e., sensitive information can be leaked by seemingly innocuous 'anonymized' or aggregate data. I will then show how to avoid these pitfalls using the framework of differential privacy. Differential privacy is an information-theoretic measure of algorithmic stability that translates into a robust privacy guarantee and which also permits us to design algorithms to perform sophisticated statistical analyses.
Privacy turns out to be intimately related to generalization in machine learning. In particular, a differentially private algorithm is guaranteed to not 'overfit' its data, meaning that any statistical conclusions extend to the underlying distribution from which the data was drawn. I will discuss this connection and explain how it is especially useful for adaptive data analysis, namely when one dataset is used over and over again and each successive analysis is informed by the outcome of previous analyses.
Host: Alessandra Scafuro, CSC