Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88385
Full metadata record
DC FieldValueLanguage
dc.contributor.authorXiao, Chunhuaen
dc.contributor.authorZhang, Leien
dc.contributor.authorLiu, Weichenen
dc.contributor.authorBergmann, Neilen
dc.contributor.authorLi, Pengdaen
dc.date.accessioned2018-08-30T06:41:25Zen
dc.date.accessioned2019-12-06T17:02:06Z-
dc.date.available2018-08-30T06:41:25Zen
dc.date.available2019-12-06T17:02:06Z-
dc.date.issued2018en
dc.identifier.citationXiao, C., Li, P., Zhang, L., Liu, W., & Bergmann, N. (2018). ACA-SDS : adaptive crypto acceleration for secure data storage in big data. IEEE Access, in press. doi:10.1109/ACCESS.2018.2862425en
dc.identifier.urihttps://hdl.handle.net/10356/88385-
dc.description.abstractIn the era of Big Data, the demand for secure data storage is rapidly increasing. To accelerate the complex encryption computation, both specific instructions and hardware accelerators are adopted in a large number of scenarios. However, the hardware accelerators are not so effective especially for small volume data due to the induced invocation costs, while the AES-NI (Intel® Advanced Encryption Standard New Instructions) is not so energy efficiency for big data. To satisfy the diversity performance/energy requirements for intensive data encryptions, a collaborative solution is proposed in this work. We proposed a feasible hardware-software co-design methodology based on the stack file system eCryptfs, with QAT (Quick Assist Technology), which is named as ACA-SDS: Adaptive Crypto Acceleration for Secure Data Storage. ACA-SDS is able to choose the optimal encryption solution dynamically according to file operation modes and request characters. Adjustable parameters, such as α, β and M are provided in our scheme to provide a better adaptability and trade-off choices for encryption computation. Our evaluation shows that ACA-SDS can get 15%-25% performance improvement for big-data blocks compared with only software or hardware accelerations. Furthermore, our methodology provides a wide range of practical design concepts for the further research in this field.en
dc.format.extent12 p.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE Accessen
dc.rights© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.en
dc.subjectEncryptionen
dc.subjectHardware-Software Co-designen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleACA-SDS : adaptive crypto acceleration for secure data storage in big dataen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.identifier.doi10.1109/ACCESS.2018.2862425en
dc.description.versionPublished versionen
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:SCSE Journal Articles
Files in This Item:
File Description SizeFormat 
ACA-SDS adaptive crypto acceleration for secure data storage in big data.pdf850.16 kBAdobe PDFThumbnail
View/Open

SCOPUSTM   
Citations 50

5
Updated on Mar 28, 2024

Web of ScienceTM
Citations 50

2
Updated on Oct 31, 2023

Page view(s) 50

511
Updated on Mar 28, 2024

Download(s) 50

114
Updated on Mar 28, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.