![]() |
COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. | ![]() |
University of Cambridge > Talks.cam > Computer Laboratory Systems Research Group Seminar > DEFCon: High-Performance Event Processing with Information Security
DEFCon: High-Performance Event Processing with Information SecurityAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Eiko Yoneki. In finance and healthcare, event processing systems handle sensitive data on behalf of many clients. Guaranteeing information security in such systems is challenging because of their strict performance requirements in terms of high event throughput and low processing latency. We describe DEFCON , an event processing system that enforces constraints on event flows between event processing units. DEFCON uses a combination of static and runtime techniques for achieving light-weight isolation of event flows, while supporting efficient sharing of events. Our experimental evaluation in a financial data processing scenario shows that DEFCON can provide information security with significantly lower processing latency compared to a traditional approach. Short Bio: Matteo Migliavacca is a Research Associate at DSE group working on the SmartFlow project after obtaining a Ph.D. degree at Politecnico di Milano with a thesis on Middleware Services for Large Scale Dynamic Distributed Systems. His main research interests are in Distributed Systems in the areas of routing, adaptivity, context-awareness and programming abstractions. This talk is part of the Computer Laboratory Systems Research Group Seminar series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsBiochem ArcDigital and CoDE talks at Anglia Ruskin Faculty of ClassicsOther talksTODAY Foster Talk - "Paraspeckles, TDP-43 & alternative polyadenylation: how regulation of a membraneless compartment guides cell fate" Autumn Cactus & Succulent Show 160 years of occupational structure: Late Imperial China and its regions Simulating Neutron Star Mergers A tale of sleepless flies and ninna nanna. How Drosophila changes what we know about sleep. Development of machine learning based approaches for identifying new drug targets |