Large technology companies rely on collecting data from their users to understand their interests, and better customize the company’s products. Increasingly, this must be done while preserving individual users’ privacy. Recently, techniques based on radomization and data sketching have been adopted to provide data collection protocols which optimize the privacy accuracy trade-off. In this talk, I’ll discuss methods deployed by Google and Apple to collect frequency information, and our recent work to monitor information on correlations in the data.
Graham Cormode a Professor in Computer Science at the University of Warwick in the UK, where he work on research topics in data management, privacy and big data analysis. Previously, he was a principal member of technical staff at AT&T Labs-Research. He is a University Liaison Director at the Alan Turing Institute, and in 2017 he was the co-recipient of Adams Prize for Mathematics for his work on Statistical Analysis of Big Data.