Thursday, November 15 • 9:50am - 10:30am
Privacy-aware data science in Scala with monads and type level programming

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In order to extract value from datasets, data science and machine learning experts require access to the data itself. However, organizations increasingly have stronger requirements for finer-grained controls over the processing and analysis of potentially sensitive data, for reasons such as regulatory compliance or general privacy policies. In machine learning applications, it may also be desirable to restrict data flow in order to avoid leakage or contamination via side channel information (eg, see Oscar Boykin's talk from last year's SBTB). We therefore seek a general mechanism to assist users in encoding and enforcing information flow policies in their software, including interactive (ie, notebook) analyses. In this talk we develop a Scala approach to this problem based on PL and security research whereby illegal data accesses can be rejected at compile-time.

avatar for David Andrzejewski

David Andrzejewski

Engineering, Sumo Logic
David Andrzejewski is a Senior Engineering Manager at Sumo Logic, where he works on applying statistical modeling and analysis techniques to machine data such as logs and metrics. He also co-organizes the SF Bay Area Machine Learning meetup group. David holds a PhD in Computer Sciences... Read More →

Thursday November 15, 2018 9:50am - 10:30am PST