scholarly journals Hardware/Software Adaptive Cryptographic Acceleration for Big Data Processing

2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Chunhua Xiao ◽  
Lei Zhang ◽  
Yuhua Xie ◽  
Weichen Liu ◽  
Duo Liu
Keyword(s):  
Big Data ◽  
Data Aggregation ◽  
Optimal Strategies ◽  
Hardware Acceleration ◽  
High Workload ◽  
Software Implementations ◽  
Symmetric Key ◽  
Software And Hardware ◽  
Hardware Encryption ◽  
Dedicated Hardware

Along with the explosive growth of network data, security is becoming increasingly important for web transactions. The SSL/TLS protocol has been widely adopted as one of the effective solutions for sensitive access. Although OpenSSL could provide a freely available implementation of the SSL/TLS protocol, the crypto functions, such as symmetric key ciphers, are extremely compute-intensive operations. These expensive computations through software implementations may not be able to compete with the increasing need for speed and secure connection. Although there are lots of excellent works with the objective of SSL/TLS hardware acceleration, they focus on the dedicated hardware design of accelerators. Hardly of them presented how to utilize them efficiently. Actually, for some application scenarios, the performance improvement may not be comparable with AES-NI, due to the induced invocation cost for hardware engines. Therefore, we proposed the research to take full advantages of both accelerators and CPUs for security HTTP accesses in big data. We not only proposed optimal strategies such as data aggregation to advance the contribution with hardware crypto engines, but also presented an Adaptive Crypto System based on Accelerators (ACSA) with software and hardware codesign. ACSA is able to adopt crypto mode adaptively and dynamically according to the request character and system load. Through the establishment of 40 Gbps networking on TAISHAN Web Server, we evaluated the system performance in real applications with a high workload. For the encryption algorithm 3DES, which is not supported in AES-NI, we could get about 12 times acceleration with accelerators. For typical encryption AES supported by instruction acceleration, we could get 52.39% bandwidth improvement compared with only hardware encryption and 20.07% improvement compared with AES-NI. Furthermore, the user could adjust the trade-off between CPU occupation and encryption performance through MM strategy, to free CPUs according to the working requirements.

scholarly journals Design of Symmetric-Key Primitives for Advanced Cryptographic Protocols

2020 ◽  
pp. 1-45 ◽  
Author(s):  
Abdelrahaman Aly ◽  
Tomer Ashur ◽  
Eli Ben-Sasson ◽  
Siemen Dhooghe ◽  
Alan Szepieniec
Keyword(s):  
Design Space ◽  
Design Strategy ◽  
Cryptographic Protocols ◽  
Crucial Point ◽  
Proof Systems ◽  
Algebraic Description ◽  
Rank One ◽  
Software Implementations ◽  
Symmetric Key ◽  
Software And Hardware

While traditional symmetric algorithms like AES and SHA-3 are optimized for efficient hardware and software implementations, a range of emerging applications using advanced cryptographic protocols such as multi-party computation and zero knowledge proofs require optimization with respect to a different metric: arithmetic complexity.In this paper we study the design of secure cryptographic algorithms optimized to minimize this metric. We begin by identifying the differences in the design space between such arithmetization-oriented ciphers and traditional ones, with particular emphasis on the available tools, efficiency metrics, and relevant cryptanalysis. This discussion highlights a crucial point—the considerations for designing arithmetization-oriented ciphers are oftentimes different from the considerations arising in the design of software- and hardware-oriented ciphers.The natural next step is to identify sound principles to securely navigate this new terrain, and to materialize these principles into concrete designs. To this end, we present the Marvellous design strategy which provides a generic way to easily instantiate secure and efficient algorithms for this emerging domain. We then show two examples for families following this approach. These families — Vision and Rescue — are benchmarked with respect to three use cases: the ZK-STARK proof system, proof systems based on Rank-One Constraint Satisfaction (R1CS), and Multi-Party Computation (MPC). These benchmarks show that our algorithms achieve a highly compact algebraic description, and thus benefit the advanced cryptographic protocols that employ them.

Deterministic binary matrix based compressive data aggregation in big data WSNs

2017 ◽  
Vol 66 (3) ◽  
pp. 345-356 ◽  
Author(s):  
Cuiye Liu ◽  
Songtao Guo ◽  
Yawei Shi ◽  
Yuanyuan Yang
Keyword(s):  
Big Data ◽  
Data Aggregation ◽  
Binary Matrix

scholarly journals Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

2021 ◽  
pp. 1-4
Author(s):  
Aakriti Shukla ◽  
◽  
Dr Damodar Prasad Tiwari ◽  
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Data Sets ◽  
Big Data Applications ◽  
High Workload ◽  
Long Time ◽  
Analytical Result ◽  
Result Analysis

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

scholarly journals A Framework for Secure Data Storage and Retrieval in Cloud Environment

2019 ◽  
Vol 9 (2) ◽  
pp. 2511-2520
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Large Volume ◽  
Service Providers ◽  
Research Work ◽  
Software Framework ◽  
Software System ◽  
Efficient Computation ◽  
Data Framework ◽  
Symmetric Key

Plenty of research work is going on for efficient storage, processing, and analysis of large volume of data generated in real time and having varying nature and quality. The most common open-source framework for efficient computation of such large volume of data is Hadoop which processes big data sets by employing clusters of networked computers. On the other hand, cloud computing refers to storage of data and applications in cloud servers and accessing of the data of applications over the Internet following an on demand scheme. So the organizations who want to reduce costs and complexities associated with big data framework, the most suitable option for them is to take help of cloud infrastructure. But one biggest concern in this regard is the security of data and applications in cloud. Though Hadoop provides in-built encryption scheme and secured HTTP protocol, once data and applications are stored in public cloud, they become vulnerable to various security breaches still remain uncontrolled by the cloud service providers giving rise of a feeling of untrust. In this scenario, encrypting sensitive business data before cloud uploading may help in preventing access of data by evil intruders. In this paper, an extension to Hadoop security with respect to shared cloud has been proposed by designing a software framework where files are encrypted before uploading to cloud. Security performance of this framework for securing data in storage as well as in transit has been implemented such that without using the framework retrieval of data is not at all possible. Extra layer of security aided by symmetric key cryptographic technique has been proposed which will enhance the security of customers’ resources along with the present standard security measures of a cloud system. A software system performs symmetric encryption before transmitting a file of any format to cloud. To access this encrypted file, the same software system has to be used to download and decrypt the file. This paper also investigates the performances of most common symmetric key techniques AES, DES and triple DES cryptography with respect to the successful encryption of the customer data. This software framework can be applied to provide an extra security layer at the client’s end for users availing service of the cloud platform.

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