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by Sihyeong Park on 2019-10-08 16:13:29

Date: 2019. 10. 10 (Thu) 19:30

Locate: EB5. 507

Presenter: Sihyeong Park

Title: StreamBox-TZ: Secure Stream Analytics at the Edge with TrustZone

AuthorHeejin Park and Shuang Zhai, Purdue ECE; Long Lu, Northeastern University; Felix Xiaozhu Lin, Purdue ECE

AbstractWhile it is compelling to process large streams of IoT data on the cloud edge, doing so exposes the data to a sophisticated, vulnerable software stack on the edge and hence security threats. To this end, we advocate isolating the data and its computations in a trusted execution environment (TEE) on the edge, shielding them from the remaining edge software stack which we deem untrusted.

This approach faces two major challenges: (1) executing high-throughput, low-delay stream analytics in a single TEE, which is constrained by a low trusted computing base (TCB) and limited physical memory; (2) verifying execution of stream analytics as the execution involves untrusted software components on the edge. In response, we present StreamBox-TZ (SBT), a stream analytics engine for an edge platform that offers strong data security, verifiable results, and good performance. SBT contributes a data plane designed and optimized for a TEE based on ARM TrustZone. It supports continuous remote attestation for analytics correctness and result freshness while incurring low overhead. SBT only adds 42.5 KB executable to the TCB (16% of the entire TCB). On an octa core ARMv8 platform, it delivers the state-of-the-art performance by processing input events up to 140 MB/sec (12M events/sec) with sub-second delay. The overhead incurred by SBT’s security mechanism is less than 25%.

 

https://www.usenix.org/conference/atc19/presentation/park-heejin

Article source: //eslab.cnu.ac.kr/en/Mobile/167-StreamBox-TZ-Secure-Stream-Analytics-at-the-Edge-with-TrustZone.html

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