In case you’re at USC or in the area, I’m giving a talk tomorrow there on some of the work I’ve been doing with Kamalika Chaudhuri (whose website seems to have moved) and Claire Monteleoni on privacy-preserving machine learning.
Learning from sensitive data – balancing accuracy and privacy
Wednesday, March 24th, 2010
The advent of electronic databases has made it possible to perform data mining and statistical analyses of populations with applications from public health to infrastructure planning. However, the analysis of individuals’ data, even for aggregate statistics, raises questions of privacy which in turn require formal mathematical analysis. A recent measure called differential privacy provides a rigorous statistical privacy guarantee to every individual in the database. We develop privacy-preserving support vector machines (SVMs) that give an improved tradeoff between misclassification error and the privacy level. Our techniques are an application of a more general method for ensuring privacy in convex optimization problems.
Joint work with Kamalika Chaudhuri (UCSD) and Claire Monteleoni (Columbia)