Scale By the Bay 2018

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.