Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88385
Title: ACA-SDS : adaptive crypto acceleration for secure data storage in big data
Authors: Xiao, Chunhua
Zhang, Lei
Liu, Weichen
Bergmann, Neil
Li, Pengda
Keywords: Encryption
Hardware-Software Co-design
DRNTU::Engineering::Computer science and engineering
Issue Date: 2018
Source: Xiao, 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.2018.2862425
Series/Report no.: IEEE Access
Abstract: In 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.
URI: https://hdl.handle.net/10356/88385
http://hdl.handle.net/10220/45763
DOI: http://dx.doi.org/10.1109/ACCESS.2018.2862425
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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