Privacy Techniques for Data Science

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.