ACA-SDS: Adaptive Crypto Acceleration for Secure Data Storage in Big Data

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 paper. We proposed a feasible hardware-software co-design methodology based on the stack file system eCryptfs, with quick assist technology, which is named adaptive crypto acceleration for secure data storage (ACA-SDS). ACA-SDS is able to choose the optimal encryption solution dynamically according to file operation modes and request characters. Adjustable parameters, such as $\alpha $ , $\beta $ , and M are provided in our scheme to provide a better adaptability and tradeoff 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.