Intelligent cryptography approach for secure distributed big data storage in cloud computing

Li, Yibin; Gai, Keke; Qiu, Longfei; Qiu, Meikang; Zhao, Hui

doi:10.1016/j.ins.2016.09.005

Cited by 201 publications

(74 citation statements)

References 41 publications

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“…Enhanced Privacy for Healthcare Insurance Guarantee Process: it is understood that a satisfactorily huge quantum PC could be incredibly powerful to deal with issues that rely upon experimentation to find an answer [14]. IOTA Tangle deals with this issue c) Accelerated clinical research areas: The advantages and utilize instances of embracing blockchain to quicken clinical research are summarized in Table 5.…”

Section: Quantum Computing Protectionmentioning

confidence: 99%

“…These techniques are put into use by a central authority; however, several decentralized techniques have been proposed to make user or data owners in control of their data [1] and [10]. This technique, however, has scalability issues based on blockchain formation process [14]. Future work will be that takes advantage a DAG Tangle [14] approach to design a framework for Enhanced Healthcare Insurance Guarantee Process.…”

Section: B Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Privacy for Big Data in Healthcare Domain based on Cryptographic and Decentralized Technology Methods

2019

IJRTE

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Privacy is one of the biggest concerns that hinder most organizations to adopt the Big Data technology. Some mechanisms and systems have been set-up to handle huge databases. Nevertheless, the scalability requirements of Big Data are far beyond the conventional databases to handle. Therefore, it is trivial to set-up scalable privacy algorithms for conventional databases. Most data are stored in a single location, which means the records it keeps are open and effortlessly irrefutable to third parties. Centralized versions of this data make it too easy for hackers to attack. As such, in this paper, we present the opportunities and challenges of implementing cryptography and blockchain for privacy perseverance in Big Data, focusing in the healthcare domain. In addition, we also present some use cases of integrating Directed Acyclic Graph (DAG) into healthcare database framework for anchoring information security and privacy.

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Section: Quantum Computing Protectionmentioning

confidence: 99%

Section: B Future Workmentioning

confidence: 99%

Enhancing Privacy for Big Data in Healthcare Domain based on Cryptographic and Decentralized Technology Methods

2019

IJRTE

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“…The method selectively encodes information using privacy classification methods under timing constraints. Besides that, other research studies [14][15][16][17][18][19][20][21][22][23][24] have also considered big data security aspects and solutions.…”

Section: Related Workmentioning

confidence: 99%

An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks

Alouneh

Al‐Hawari

Hababeh

et al. 2018

Security and Communication Networks

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The need for effective approaches to handle big data that is characterized by its large volume, different types, and high velocity is vital and hence has recently attracted the attention of several research groups. This is especially the case when traditional data processing techniques and capabilities proved to be insufficient in that regard. Another aspect that is equally important while processing big data is its security, as emphasized in this paper. Accordingly, we propose to process big data in two different tiers. The first tier classifies the data based on its structure and on whether security is required or not. In contrast, the second tier analyzes and processes the data based on volume, variety, and velocity factors. Simulation results demonstrated that using classification feedback from a MPLS/GMPLS core network proved to be key in reducing the data evaluation and processing time.

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“…The proposed architecture was developed to secure the data and the applications deployed on the cloud. The authors of [28] and [31] proposed a novel architectural model that works by splitting the files into multiple small parts and storing them on distinct storage servers on the cloud. They named their approach as "Security-Aware Efficient Distributed Storage (SAEDS)" model.…”

Section: B Related Workmentioning

confidence: 99%

PPS-ADS: A Framework for Privacy-Preserved and Secured Distributed System Architecture for Handling Big Data

Ahad

Biswas

2018

International Journal on Advanced Science, Engineering and Information Technology

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The exponential expansion of Big Data in 7V's (velocity, variety, veracity, value, variability, and visualization) brings forth new challenges to security, reliability, availability, and privacy of these data sets. Traditional security techniques and algorithms fail to complement this big gigantic data. This paper aims to improve the recently proposed Atrain Distributed System (ADS) by incorporating new features, which will cater to the end-to-end availability and security aspects of the big data in the distributed system. The paper also integrates the concept of Software Defined Networking (SDN) in ADS to effectively control and manage the routing of the data item in the ADS. The storage of data items in the ADS is done based on the type of data (structured or unstructured), the capacity of the distributed system (or coach) and the distance of coach from the pilot computer (PC). In order to maintain the consistency of data and to eradicate the possible loss of data, the concept of "forward positive" and "backward positive" acknowledgment is proposed. Furthermore, we have incorporated "Twofish" cryptographic technique to encrypt the big data in the ADS. Issues like "data ownership," "data security, "data privacy" and data reliability" are pivotal while handling the big data. The current paper presents a framework for a privacy-preserved architecture for effectively handling the big data.

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Intelligent cryptography approach for secure distributed big data storage in cloud computing

Cited by 201 publications

References 41 publications

Enhancing Privacy for Big Data in Healthcare Domain based on Cryptographic and Decentralized Technology Methods

Enhancing Privacy for Big Data in Healthcare Domain based on Cryptographic and Decentralized Technology Methods

An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks

PPS-ADS: A Framework for Privacy-Preserved and Secured Distributed System Architecture for Handling Big Data

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