Differential Privacy in the Wild: New problems in private data analysis
||Thursday, October 20, 2016
||12:00pm - 1:00pm
||D344 LSRC, Duke
Privacy is an important constraint that data analysis techniques must satisfy in today's data driven world. Differential privacy has arisen as a gold standard for privacy, and is starting to see adoption in many commercial (e.g., Google and Apple) and government entities (e.g., US Census) for collecting user data and sharing it privately. In today's talk I will highlight some of the open challenges in designing differentially private algorithms for emerging applications, and highlight research at Duke that try to address these challenges.
Ashwin Machanavajjhala is an assistant professor at Duke University. His primary research interests lie in privacy in statistical databases, privacy in augmented reality systems, and systems for massive data analytics. Previously, he was a Senior Research Scientist at Yahoo! Research.