Machine learning concepts for correlated Big Data privacy

With data becoming a salient asset worldwide, dependence amongst data kept on growing. Hence the real-world datasets that one works upon in today’s time are highly correlated. Since the past few years, researchers have given attention to this aspect of data privacy and found a correlation among data. The existing data privacy guarantees cannot assure the expected data privacy algorithms. The privacy guarantees provided by existing algorithms were enough when there existed no relation between data in the datasets. Hence, by keeping the existence of data correlation into account, there is a dire need to reconsider the privacy algorithms. Some of the research has considered utilizing a well-known machine learning concept, i.e., Data Correlation Analysis, to understand the relationship between data in a better way. This concept has given some promising results as well. Though it is still concise, the researchers did a considerable amount of research on correlated data privacy. Researchers have provided solutions using probabilistic models, behavioral analysis, sensitivity analysis, information theory models, statistical correlation analysis, exhaustive combination analysis, temporal privacy leakages, and weighted hierarchical graphs. Nevertheless, researchers are doing work upon the real-world datasets that are often large (technologically termed big data) and house a high amount of data correlation. Firstly, the data correlation in big data must be studied. Researchers are exploring different analysis techniques to find the best suitable. Then, they might suggest a measure to guarantee privacy for correlated big data. This survey paper presents a detailed survey of the methods proposed by different researchers to deal with the problem of correlated data privacy and correlated big data privacy and highlights the future scope in this area. The quantitative analysis of the reviewed articles suggests that data correlation is a significant threat to data privacy. This threat further gets magnified with big data. While considering and analyzing data correlation, then parameters such as Maximum queries executed, Mean average error values show better results when compared with other methods. Hence, there is a grave need to understand and propose solutions for correlated big data privacy.

Machine Learning Concepts for Correlated Big Data Privacy

With data becoming a salient asset worldwide, dependence within data kept on growing, hence the real world datasets that one works upon in today's time are highly correlated. Since the past few years, researchers have given attention to this aspect of data privacy and found that where there exists a correlation among data, the existing privacy guarantees could not be assured with existing privacy algorithms. The privacy guarantees provided by existing algorithms were enough when there existed no relation between data in the datasets. Hence, by keeping the existence of data correlation into account, there is a dire need, to reconsider the privacy algorithms. Some of the research have considered to utilize a well known machine learning concept, i.e., Data Correlation Analysis to understand the relationship between data in a better way. This has given some promising results as well. Though its less but still a considerable amount of research has been done for correlated data privacy. But correlated big data privacy is very less explored. The real world datasets that are worked upon, are often large in size (technologically termed as big data) and house a high amount of data correlation. Hence, there is a grave need to understand and propose solutions for correlated big data privacy.

Review: Big Data Privacy and Security Risk and Solutions

Big Data is very dynamic issues in the current year, enables computing resources as a data to be provided as Information Technology services with high efficiency and effectiveness. The high amount of data in world is growing day by day. Data is growing very rapidly because of use of internet, smart phone and social network. Now size of the data is in Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyze this large amount of data. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the limits. However, the current scenario the growth rate of such large data creates number of challenges, such as the fast growth of data, access speed, diverse data, and security. This paper shows the fundamental concepts of Big Data. Privacy threats and security methods used in Big Data. With the development of various research application and recourses of Internet/Mobile Internet, social networks, Internet of Things, big data has become the very important topic of research across the world, at the same time, big data has security risks and privacy protection during different stages such as collecting, storing, analyzing and utilizing. This paper introduces security measures of big data, then proposes the technology to solve the security threats.

Security and Privacy in Big Data Computing

The rapid growth of digitization in the present era leads to an exponential increase of information which demands the need of a Big Data paradigm. Big Data denotes complex, unstructured, massive, heterogeneous type data. The Big Data is essential to the success in many applications; however, it has a major setback regarding security and privacy issues. These issues arise because the Big Data is scattered over a distributed system by various users. The security of Big Data relates to all the solutions and measures to prevent the data from threats and malicious activities. Privacy prevails when it comes to processing personal data, while security means protecting information assets from unauthorized access. The existence of cloud computing and cloud data storage have been predecessor and conciliator of emergence of Big Data computing. This article highlights open issues related to traditional techniques of Big Data privacy and security. Moreover, it also illustrates a comprehensive overview of possible security techniques and future directions addressing Big Data privacy and security issues.