Úlfar currently heads a security research team at Google. Previously, he has been a researcher at Microsoft Research, Silicon Valley,
an Associate Professor at Reykjavik University, Iceland, and led security technology at two startups: GreenBorder and deCODE Genetics.
He holds a PhD in computer science from Cornell University.

SWS, RG1, MMCI   Info

Expert Audience

English

90 Minutes

Saarbrücken

For computer software, our security models, policies, mechanisms, and means of assurance were primarily conceived and developed before the end of the 1970's. However, since that time, software has changed radically: it is thousands of times larger, comprises countless libraries, layers, and services, and is used for more purposes, in far more complex ways. This suggests that we should revisit some of our core computer security concepts. For example, what does the Principle of Least Privilege mean when all software contains libraries that can express arbitrary functionality? And, what security policy should be enforced when software is too complex for either its developers or its users to explain its intended behavior in detail?
One possibility is to take an empirical, data-driven approach to modern software, and determine its exact, concrete behavior via comprehensive, online monitoring. Such an approach can be a practical, effective basis for security—as demonstrated by its success in spam and abuse fighting—but its use to constrain software behavior raises many questions. In particular, two questions seem critical. First, is it possible to learn the details of how software *is* behaving, without intruding on the privacy of its users?  Second, are those details a good foundation for deriving security policies that constrain how software *should* behave?  This talk answers both these questions in the affirmative, as part of an overall approach to data-driven security. Specifically, the talk describes techniques for learning detailed software statistics while providing differential privacy for its users, and how deep learning can help derive useful security policies that match users' expectations with intended software behavior. Those techniques are both practical and easy to adopt, and have already been used at scale for billions of users.

Claudia Richter

9303 9103

--email hidden

Yes

Kaiserslautern

G26

111

passcode not visible

logged in users only