PubSub-SGX: Exploiting Trusted Execution Environments for Privacy-Preserving Publish/Subscribe Systems
| Autor: | Pascal Felber, Valerio Schiavoni, Christof Fetzer, Fábio Silva, Franz Gregor, Wojciech Ozga, Andre Martin, Marcus Tenorio, Sergei Arnautov, Nikolaus Thummel, Robert Krahn, Andrey Brito |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
010504 meteorology & atmospheric sciences Exploit Computer science business.industry Cloud computing 02 engineering and technology Python (programming language) computer.software_genre 01 natural sciences Computer Science - Distributed Parallel and Cluster Computing Scalability 0202 electrical engineering electronic engineering information engineering Operating system 020201 artificial intelligence & image processing Confidentiality Distributed Parallel and Cluster Computing (cs.DC) business computer Publication Information exchange 0105 earth and related environmental sciences computer.programming_language Anonymity |
| Zdroj: | 2018 IEEE 37th Symposium on Reliable Distributed Systems (SRDS) SRDS |
| Popis: | This paper presents PUBSUB-SGX, a content-based publish-subscribe system that exploits trusted execution environments (TEEs), such as Intel SGX, to guarantee confidentiality and integrity of data as well as anonymity and privacy of publishers and subscribers. We describe the technical details of our Python implementation, as well as the required system support introduced to deploy our system in a container-based runtime. Our evaluation results show that our approach is sound, while at the same time highlighting the performance and scalability trade-offs. In particular, by supporting just-in-time compilation inside of TEEs, Python programs inside of TEEs are in general faster than when executed natively using standard CPython. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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