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Conference Paper: SecDATAVIEW: A Secure Big Data Workflow Management System for Heterogeneous Computing Environments

TitleSecDATAVIEW: A Secure Big Data Workflow Management System for Heterogeneous Computing Environments
Authors
Issue Date2019
PublisherAssociation for Computing Machinery. The Proceedings of the web site is located at https://dl.acm.org/citation.cfm?id=3359789
Citation
Proceedings of the 35th Annual Computer Security Applications Conference 2019 (ACSAC 2019), San Juan, Puerto Rico, 9-13 December 2019, p. 390-403 How to Cite?
AbstractBig data workflow management systems (BDWFMSs) have recently emerged as popular platforms to perform large-scale data analytics in the cloud. However, the protection of data confidentiality and secure execution of workflow applications remains an important and challenging problem. Although a few data analytics systems were developed to address this problem, they are limited to specific structures such as Map-Reduce-style workflows and SQL queries. This paper proposes SecDATAVIEW, a BDWFMS that leverages Intel Software Guard eXtensions (SGX) and AMD Secure Encrypted Virtualization (SEV) to develop a heterogeneous trusted execution environment for workflows. SecDATAVIEW aims to (1) provide the confidentiality and integrity of code and data for workflows running on public untrusted clouds, (2) minimize the TCB size for a BDWFMS, (3) enable the trade-off between security and performance for workflows, and (4) support the execution of Java-based workflow tasks in SGX. Our experimental results show that SecDATAVIEW imposes $1.69x$ to $2.62x$ overhead on workflow execution time on SGX worker nodes, $1.04x$ to $1.29x$ overhead on SEV worker nodes, and $1.20x$ to $1.43x$ overhead on a heterogeneous setting in which both SGX and SEV worker nodes are used.
DescriptionSession: Big Data Security
Persistent Identifierhttp://hdl.handle.net/10722/277271
ISBN

 

DC FieldValueLanguage
dc.contributor.authorMofrad, S-
dc.contributor.authorAhmed, I-
dc.contributor.authorLu, S-
dc.contributor.authorYang, P-
dc.contributor.authorCui, H-
dc.contributor.authorZhang, F-
dc.date.accessioned2019-09-20T08:47:53Z-
dc.date.available2019-09-20T08:47:53Z-
dc.date.issued2019-
dc.identifier.citationProceedings of the 35th Annual Computer Security Applications Conference 2019 (ACSAC 2019), San Juan, Puerto Rico, 9-13 December 2019, p. 390-403-
dc.identifier.isbn978-1-4503-7628-0-
dc.identifier.urihttp://hdl.handle.net/10722/277271-
dc.descriptionSession: Big Data Security-
dc.description.abstractBig data workflow management systems (BDWFMSs) have recently emerged as popular platforms to perform large-scale data analytics in the cloud. However, the protection of data confidentiality and secure execution of workflow applications remains an important and challenging problem. Although a few data analytics systems were developed to address this problem, they are limited to specific structures such as Map-Reduce-style workflows and SQL queries. This paper proposes SecDATAVIEW, a BDWFMS that leverages Intel Software Guard eXtensions (SGX) and AMD Secure Encrypted Virtualization (SEV) to develop a heterogeneous trusted execution environment for workflows. SecDATAVIEW aims to (1) provide the confidentiality and integrity of code and data for workflows running on public untrusted clouds, (2) minimize the TCB size for a BDWFMS, (3) enable the trade-off between security and performance for workflows, and (4) support the execution of Java-based workflow tasks in SGX. Our experimental results show that SecDATAVIEW imposes $1.69x$ to $2.62x$ overhead on workflow execution time on SGX worker nodes, $1.04x$ to $1.29x$ overhead on SEV worker nodes, and $1.20x$ to $1.43x$ overhead on a heterogeneous setting in which both SGX and SEV worker nodes are used.-
dc.languageeng-
dc.publisherAssociation for Computing Machinery. The Proceedings of the web site is located at https://dl.acm.org/citation.cfm?id=3359789-
dc.relation.ispartofAnnual Computer Security Applications Conference-
dc.titleSecDATAVIEW: A Secure Big Data Workflow Management System for Heterogeneous Computing Environments-
dc.typeConference_Paper-
dc.identifier.emailCui, H: heming@cs.hku.hk-
dc.identifier.authorityCui, H=rp02008-
dc.identifier.doi10.1145/3359789.3359845-
dc.identifier.scopuseid_2-s2.0-85077811180-
dc.identifier.hkuros305864-
dc.identifier.spage390-
dc.identifier.epage403-
dc.publisher.placeNew York, NY-

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