Demand is increasing for technology companies to safeguard individual data. This demand is leading to data regulations, algorithm accountability and systems that purposely add noise to data to protect privacy. This presentation examines data privacy regulations currently in place, and teaches data privacy algorithms such as K-anonymization, Randomized Response, and Differential Privacy. Moreover, it will cover how to perform analysis with differentially private query systems and conclude with the impact data privacy has on Machine Learning performance.
Jim Klucar is the Director of Data Science at Immuta, the fastest way for algorithm-driven enterprises to accelerate the development and control of machine learning and advanced analytics. After a dozen years of developing high performance radar processing techniques, in 2010 he switched to developing Hadoop-based data warehouse and analysis systems. Jim holds a BS in Electrical Engineering from Pennsylvania State University and a MS in Applied and Computational Mathematics from Johns Hopkins University.