scholarly journals Novel Stream Ciphering Algorithm for Big Data Images Using Zeckendorf Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Liangshun Wu ◽  
Hengjin Cai
Keyword(s):  
Big Data ◽  
Stream Cipher ◽  
Large Data ◽  
Security And Privacy ◽  
Data Sets ◽  
Imaging Data ◽  
High Definition ◽  
High Data ◽  
Huge Data ◽  
Data Volume

Big data is a term used for very large data sets. Digital equipment produces vast amounts of images every day; the need for image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients’ medical imaging data in cloud disk. There is an obvious contradiction between the security and privacy and the widespread use of big data. Nowadays, the most important engine to provide confidentiality is encryption. However, block ciphering is not suitable for the huge data in a real-time environment because of the strong correlation among pixels and high redundancy; stream ciphering is considered a lightweight solution for ciphering high-definition images (i.e., high data volume). For a stream cipher, since the encryption algorithm is deterministic, the only thing you can do is to make the key “look random.” This article proves that the probability that the digit 1 appears in the midsection of a Zeckendorf representation is constant, which can be utilized to generate the pseudorandom numbers. Then, a novel stream cipher key generator (ZPKG) is proposed to encrypt high-definition images that need transferring. The experimental results show that the proposed stream ciphering method, with the keystream of which satisfies Golomb’s randomness postulates, is faster than RC4 and LSFR with indistinguishable performance on hardware depletion, and the method is highly key sensitive and shows good resistance against noise attacks and statistical attacks.

scholarly journals Large Dataset Classification Using Parallel Processing Concept

2020 ◽  
Vol 4 (4) ◽  
pp. 191
Author(s):  
Mohammad Aljanabi ◽  
Hind Ra'ad Ebraheem ◽  
Zahraa Faiz Hussain ◽  
Mohd Farhan Md Fudzee ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Big Data Analytics ◽  
Large Data ◽  
Business Analytics ◽  
Healthcare Applications ◽  
Current Increase ◽  
High Data ◽  
Huge Data ◽  
Data Volume ◽  
Effective Decision Making ◽  
Experimental Findings

Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier.

scholarly journals Big data management: Security and privacy concerns

2021 ◽  
Vol 8 (5) ◽  
pp. 73-83
Author(s):  
Ibrahim A. Atoum ◽  
◽  
Ismail M. Keshta ◽  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Large Data ◽  
Security And Privacy ◽  
Data Sets ◽  
Privacy Concerns ◽  
Software Companies ◽  
Systemic Analysis ◽  
Functional Areas ◽  
Privacy Issues

Big data has been used by different companies to deliver simple products and provide enhanced customer insights through predictive technology such as artificial intelligence. Big data is a field that mainly deals with the extraction and systemic analysis of large data sets to help businesses discover trends. Today, many companies use Big Data to facilitate growth in different functional areas as well as expand their ability to handle large customer databases. Big data has grown the demand for information management experts such that many software companies are increasingly investing in firms that specialize in data management and analytics. Nevertheless, the issue of data protection or privacy is a threat to big data management. This article presents some of the major concerns surrounding the application and use of Big Data about challenges of security and privacy of data stored on technological devices. The paper also discusses some of the current studies being undertaken aimed at addressing security and privacy issues in Big Data.

scholarly journals A distributed big data library extending Java 8

2017 ◽  
Vol 7 (1.1) ◽  
pp. 237
Author(s):  
MD. A R Quadri ◽  
B. Sruthi ◽  
A. D. SriRam ◽  
B. Lavanya
Keyword(s):  
Big Data ◽  
Distributed Computing ◽  
Programming Model ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Distributed Environment ◽  
Multiple Systems ◽  
Huge Data ◽  
Distributed Streams

Java is one of the finest language for big data because of its write once and run anywhere nature. The new release of java 8 introduced few strategies like lambda expressions and streams which are helpful for parallel computing. Though these new strategies helps in extracting, sorting and filtering data from collections and arrays, still there are problems with it. Streams cannot properly process with the large data sets like big data. Also, there are few problems associated while executing in distributed environment. The new streams introduced in java are restricted to computations inside the single system there is no method for distributed computing over multiple systems. And streams store data in their memory and therefore cannot support huge data sets. Now, this paper cope with java 8 behalf of massive data and deed in distributed environment by providing extensions to the Programming model with distributed streams. The distributed computing of large data programming models may be consummated by introducing distributed stream frameworks.

scholarly journals A Study on Machine Learning in Big Data

2017 ◽  
Vol 10 (3) ◽  
pp. 660-663
Author(s):  
L. Dhanapriya ◽  
Dr. S. MANJU
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Huge Data ◽  
Learning Techniques ◽  
Market Needs

In the recent development of IT technology, the capacity of data has surpassed the zettabyte, and improving the efficiency of business is done by increasing the ability of predictive through an efficient analysis on these data which has emerged as an issue in the current society. Now the market needs for methods that are capable of extracting valuable information from large data sets. Recently big data is becoming the focus of attention, and using any of the machine learning techniques to extract the valuable information from the huge data of complex structures has become a concern yet an urgent problem to resolve. The aim of this work is to provide a better understanding of this Machine Learning technique for discovering interesting patterns and introduces some machine learning algorithms to explore the developing trend.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

The Novel Multi-layered Approach to Enhance Sorting Performance of Healthcare Analysis

2022 ◽  
Vol 11 (3) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Data ◽  
The Novel ◽  
Analytical Technique ◽  
Huge Data ◽  
Costs Of Treatment ◽  
Layered Approach ◽  
New Challenges

Emergence of big data in today’s world leads to new challenges for sorting strategies to analyze the data in a better way. For most of the analyzing technique, sorting is considered as an implicit attribute of the technique used. The availability of huge data has changed the way data is analyzed across industries. Healthcare is one of the notable areas where data analytics is making big changes. An efficient analysis has the potential to reduce costs of treatment and improve the quality of life in general. Healthcare industries are collecting massive amounts of data and look for the best strategies to use these numbers. This research proposes a novel non-comparison based approach to sort a large data that can further be utilized by any big data analytical technique for various analyses.

Big Data

2015 ◽  
pp. 326-344
Author(s):  
Piyush Kumar Shukla ◽  
Madhuvan Dixit
Keyword(s):  
Big Data ◽  
Large Volume ◽  
Knowledge Engineering ◽  
Complex Structure ◽  
Security And Privacy ◽  
Data Sets ◽  
User Interest ◽  
Image Information ◽  
Aggregation Of Information ◽  
Technical Area

In this chapter, Big Data provide large-volume, complex structure, heterogeneous and irregular growing data sets include multiple and autonomous different resources. In this chapter, With the growing improvement of networking sites, image information storing capacity become big issue too, Big Data concept are most growing expanding in all technical area and knowledge engineering domains, including physical, medical and paramedical sciences. Here a data-driven method consist demand-driven aggregation of information and knowledge mining and analysis, user interest prototyping, security and privacy aspects has been presented.

A Detailed Study on Classification Algorithms in Big Data

2020 ◽  
pp. 30-46
Author(s):  
Saranya N. ◽  
Saravana Selvam
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Linear Regression ◽  
Comprehensive Evaluation ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Classification Methods ◽  
Computing Science ◽  
Data Collections

After an era of managing data collection difficulties, these days the issue has turned into the problem of how to process these vast amounts of information. Scientists, as well as researchers, think that today, probably the most essential topic in computing science is Big Data. Big Data is used to clarify the huge volume of data that could exist in any structure. This makes it difficult for standard controlling approaches for mining the best possible data through such large data sets. Classification in Big Data is a procedure of summing up data sets dependent on various examples. There are distinctive classification frameworks which help us to classify data collections. A few methods that discussed in the chapter are Multi-Layer Perception Linear Regression, C4.5, CART, J48, SVM, ID3, Random Forest, and KNN. The target of this chapter is to provide a comprehensive evaluation of classification methods that are in effect commonly utilized.

Uncertainty-Based Clustering Algorithms for Large Data Sets

2018 ◽  
pp. 1-33 ◽  
Author(s):  
B. K. Tripathy ◽  
Hari Seetha ◽  
M. N. Murty
Keyword(s):  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Mining Machine ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
New Algorithms

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.

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