Discuss about the Information Security for Privacy and Security Issues.
The Big data is one of the emerging areas that are used to manage datasets, whose size is beyond the capability of commonly used software tools for capturing, managing and analyzing the amount of data. According to Sagiroglu and Sinanc ( 2013), conventional security systems are tailored to computer infrastructures which are confined with well defined perimeter of security as the public clouds helps big data in expanding.
The report endorses the significant aspects that are related with the security and privacy of big data. The contents of the report helps in unearthing the challenges that are related with big data, relevant technologies associated with big data, its applications and their impacts.
According to Kaisler et al. ( 2013), with the increase in accessibility of big data, privacy as well as security concerns are growing day by day. Data sharing has become one of the most significant tasks before the governmental agencies, scientist and businessman. The technologies as well as tools are developed for managing the data sets which are not designed for proper privacy or security measures. On the other hand, Kim et al. (2014) argued that the tools and technologies are not incorporated adequately for security measures due to lack of training and fundamental understanding about the requirement. The procedure of big data also lacks adequate policies for ensuring compliance with the present approaches of privacy as well as security (Hashem et al., 2015). The present technological advancement towards privacy or security of data is increasingly being breached. It is done either intentionally or it happens accidentally, therefore the most important need is to update the present approaches in order to prevent the challenges and issues that are associated with the process of data leaking.
The Big Data is an area of risk that needs to be considered as it includes lifecycle which is associated with the ownership as well as classification of data on the basis of collection and creation procedure and lack of security processes (Riggins & Wamba, 2015). As Big Data is one of the significant as well as complex topic it is always associated with the challenges and security issues. The challenges of Big Data have a direct impact on the designs of security issues that are required for tackling the characteristics and requirements.
According to Kshetri (2014), CSA has divided the different challenges of big data which are associated with the privacy as well as security issues in four different aspects of the big data ecosystems. The aspects include security of Infrastructure, Data management, Data security Reactive security and Integrity. Each of the aspects faces lot of security issues which includes secured Distributed Data processing, Granular audits, Data security associated with Cryptographic solutions, Secure storage and Transaction logs associated with data and many more (Chaudhuri, 2015). All this security as well as the challenges that are related with the privacy of Big Data covers the whole spectrum of the cycle of Big Data, Its sources of data production, Storage and processing of data, data transportation and storage on different devices. On the other hand, Tene and Polonetsky (2012) argued that a specific aspect of Big data security and privacy needs to be related with the rise of internet of things. The increase in the number of connected devices has led the manufacturers in the market for short period of time for exploiting the opportunity. It helps in providing tremendous benefit and opportunities to the users who are responsible for security or privacy challenges.
According to Inukollu et al. (2014), there are many identified security issues which are associated with the insecure web interface, insufficient authentication and insure services of network. Insecure web interface allows an attacker to exploit the web interface of the administration and thus it helps in unauthorized access to control the “internet of things” device. Insufficient authorization also raises privacy concerns. It allows a hacker to exploit the policy of password in order to access the privileged mode on the devices of IoT (Marx, 2013). Insecure network services exploits services of the devices that are related with the devices of “Internet of Things”. On the other hand, Grolinger et al. (2014) stated that few more privacy concerns or challenges that are related with the security system of Big Data. The challenges are due to insecure interface of mobile, insufficient configurability of security and insecure cloud interface. Due to lack of configuration, an attacker can easily access the data or have control on the devices (Action et al., 2014).It is also stated that without effective security control an hacker or attacker can use various vectors such as account enumeration, insufficient authentication for accessing data with the help of the mobile interface.
The topic of big data encompasses many trends which includes development of new technologies that helps the users to consider and handle the Big Data properly. According to Wu et al. (2014), there is no comprehensive Big Data technology for resolving the challenges because the big data project companies are very much different from one another therefore, a proven complete certification is not yet provided although some of the vendors like IBM have announced several programs related with certification. On the other hand, Boyd and Crawford (2012) stated that hadoop is synonymous with the term big data and it is very much famous for handing huge amount of data. The Distributed file system of Hadoop helps in enabling highly scalable as well as redundant storage of data for executing various types of projects. Analytical databases are used for the purpose of data processing. Many of the techniques use connectors in order to integrate with the system of Hadoop (Dou et al., 2015). The technology of big data is divided into two components which are software and hardware component. The hardware component of the structure consists of infrastructure layer whereas the software part or the component is categorized into management software, discovery and analytics software, automation and decision support software.
According to Kaushik and Jain (2014), Infrastructure is considered to be the foundation of Big Data technology stack. The main components that are very much necessary for the storage procedure includes standard of the industry, servers and networking bandwidth of about 10 Gbps. The storage systems are designed in a very much flexible way for supporting capabilities in memory delivered systems.
On the other hand, Wu et al. (2014) stated that the layers that are associated with the processes of the software and prepares both structured as well as unstructured analysis helps in extracting, normalizing and integrating data. The architectures of data management and organization include RDBMS (“Relational Database Management System” ) and the NoSQL database management system (Action et al., 2014). The database management systems are designed in order to manage different types of data.
According to Grolinger et al. (2014), Apache Hadoop has several applications in lowering the cost barriers that are related with processing and analyzing of big data. Technical barriers remain but the applications that are related with the Hadoop system are highly complex. On the other hand, Boyd and Crawford (2012) stated that there are many application of Hadoop system. It is mainly used in analyzing life-threatening risks, warning signs for security breaches and also helps in preventing hardware failure.
According to Marx (2013), machines create a lot of information in order to explore the applications of Hadoop. Capturing data from HVAC systems helps in identifying problems with locations and products. On the other hand, Tene and Polonetsky (2012) argued that hadoop are used in streaming projects, complex event processing, replacing SAS. In order to make the Hadoop applications accessible thoroughly to the organizations, the system needs to be integrated for the overall flow of data.” Talend Open Studio” is one of the ideal tools that help in integrating the application inside the architecture of data (Chaudhuri, 2015).It helps in providing more built-in connector components than any other integration of data. The connectors help in writing in any format, database or packed enterprise application.
According to Kaushik and Jain (2014), despite of the presence of Big Data technologies that are available in the market, enterprises are struggling a lot in order to take proper advantage of the big data. It is because the organizations fail to fulfill certain criteria’s which include implementing mechanism for combining data from different sources and proper industrializing of the entire data (Grolinger et al., 2014). Combining technology stacks for facilitating successful effective aggregation, analysis, ingestion and combining data for providing ROI for the implementation purpose of Big Data. The organizations must have to jump over some of the hurdles for implementing effective and proper strategies that must be related with Big Data.
On the other hand, Riggins and Wamba (2015) stated that for resolving the challenges the enterprises needs to follow some steps. It includes codifying problems that are solved with the help of Big Data. The experts of the enterprise must need to agree upon certain criterion which helps in explaining the type of data that is collected and its sources from where the data is collected. The resolving procedure also includes creation of right data that are required for the core implementation by processing the collected data (Tene and Polonetsky, 2012). The enterprises always increase the size of the data sample without taking much time for verifying whether the model is accurate or not. If the data model is tested and the test is successful, then also the enterprises have to be careful.
Conclusion
It can be concluded that the Big Data faces lot of challenges due to privacy or security issues. There is lot of challenges which needs to solve in order to mitigate the issues. The Big Data is an area of risk that desires to be considered as it includes lifecycle which is related with the possession as well as categorization of data on the basis of gathering and formation procedure and lack of security processes. It is analyzed that there are several gaps in the technologies that are used for managing and processing Big Data analytics. Therefore proper steps and measures needs to be considered in order to reduce the gaps as well as in mitigating the challenges.
References
Action, C., Watchdog, C., Rights, P. P., Clearinghouse, P. R., & American Library Association. (2014). Coalition Letter to Director Holden to Petition for OSTP to Conduct a Public Comment Process on Big Data and the Future of Privacy.
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.
Chaudhuri, S. (2012, May). What next?: a half-dozen data management research goals for big data and the cloud. In Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems(pp. 1-4). ACM.
Dou, W., Zhang, X., Liu, J., & Chen, J. (2015). HireSome-II: Towards privacy-aware cross-cloud service composition for big data applications.IEEE Transactions on Parallel and Distributed Systems, 26(2), 455-466.
Grolinger, K., Hayes, M., Higashino, W. A., L’Heureux, A., Allison, D. S., & Capretz, M. A. (2014, June). Challenges for mapreduce in big data. In 2014 IEEE World Congress on Services (pp. 182-189). IEEE.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.
Inukollu, V. N., Arsi, S., & Ravuri, S. R. (2014). Security issues associated with big data in cloud computing. International Journal of Network Security & Its Applications, 6(3), 45.
Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013, January). Big data: issues and challenges moving forward. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 995-1004). IEEE.
Kaushik, M., & Jain, A. (2014). Challenges to Big Data Security and Privacy.International Journal of Computer Science and Information Technologies (IJCSIT), 5(3), 3042-3043.
Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.
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Marx, V. (2013). Biology: The big challenges of big data. Nature, 498(7453), 255-260.
Riggins, F. J., & Wamba, S. F. (2015, January). Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In System Sciences (HICSS), 2015 48th Hawaii International Conference on (pp. 1531-1540). IEEE.
Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
Tene, O., & Polonetsky, J. (2012). Big data for all: Privacy and user control in the age of analytics. Nw. J. Tech. & Intell. Prop., 11, xxvii.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data.IEEE transactions on knowledge and data engineering, 26(1), 97-107.
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