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Privacy Issues with Machine Learning

Privacy Issues with Machine Learning: Fears, Facts, and Opportunities by Chris Clifton - Machine Learning Summer School at Purdue, 2011. Increasing collection of data, and increased ability of machines to understand it, have lead to highly public privacy concerns. Call it "data mining" instead of "machine learning", and you might even find your funding cut... This talk will briefly review the history and legal background of privacy issues in machine learning. We will then look at a sampling of specific challenges and solutions where privacy concerns and machine learning interact. Each will be capped with a discussion of new research opportunities and what it takes to work in the area.

Privacy Issues with Machine Learning: Fears, Facts, and Opportunities


Machine Learning Summer School at Purdue, 2011
A Machine Learning Approach for Complex Information Retrieval Applications
A Short Course on Reinforcement Learning
Classic and Modern Data Clustering
Divide and Recombine for the Analysis of Big Data
Graphical Models for the Internet
Introduction to Machine Learning
Large-Scale Machine Learning and Stochastic Algorithms
Machine Learning for a Rainy Day
Machine Learning for Discovery in Legal Cases
Machine Learning for Statistical Genetics
Mining Heterogeneous Information Networks
Modeling Complex Social Networks
Optimization for Machine Learning
Privacy Issues with Machine Learning: Fears, Facts, and Opportunities
Survey of Boosting from an Optimization Perspective
The MASH Project