It is worth noting that label(s) is built from information available at (DH) and (DSD). When considering a big data solution, you can best mitigate the risks through strategies such as employee training and varied encryption techniques. (vi)Security and sharing: this process focuses on data privacy and encryption, as well as real-time analysis of coded data, in addition to practical and secure methods for data sharing. Traffic that comes from different networks is classified at the gateway of the network responsible to analyze and process big data. Classifying big data according to its structure that help in reducing the time of applying data security processes. (iii)Transferring big data from one node to another based on short path labels rather than long network addresses to avoid complex lookups in a routing table. Consequently, the gateway is responsible for distributing the labeled traffic to the appropriate node (NK) for further analysis and processing at Tier 2. In other words, this tier decides first on whether the incoming big data traffic is structured or unstructured. Thus, the use of MPLS labels reduces the burden on tier node(s) to do the classification task and therefore this approach improves the performance. The Gateways are responsible for completing and handling the mapping in between the node(s), which are responsible for processing the big data traffic arriving from the core network. Our proposed method has more success time compared to those when no labeling is used. The proposed classification algorithm is concerned with processing secure big data. In case encryption is needed, it will be supported at nodes using appropriate encryption techniques. Big data is a new term that refers not only to data of big size, but also to data with unstructured characteristic types (i.e., video, audio, unstructured text, and social media information). An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. However, more institutions (e.g. The effect of labeling implementation on the total nodal processing time for big data analysis has been shown in Figure 6. The proposed algorithm relies on different factors for the analysis and is summarized as follows:(i)Data Source and Destination (DSD): data source as well as destination may initially help to guess the structure type of the incoming data. In [8], they proposed to handle big data security in two parts. Then, it checks the type of security service that is applied on the data, i.e., whether encryption is applied or not on the processed data, or if authentication is implemented or required on the processed data. In this special issue, we discuss relevant concepts and approaches for Big Data security and privacy, and identify research challenges to be addressed to achieve comprehensive solutions. Executive Office of the President, “Big Data Across the Federal Government,” WH official website, March 2012. (iii)Tier 2 is responsible to process and analyze big data traffic based on Volume, Velocity, and Variety factors. The GMPLS extends the architecture of MPLS by supporting switching for wavelength, space, and time switching in addition to the packet switching. We have chosen different network topologies with variable distances between nodes ranging from 100m to 4000Km in the context of wired networks (LAN, WAN, MAN). The COVID-19 pandemic leads governments around the world to resort to tracking technology and other data-driven tools in order to monitor and curb the spread of SARS-CoV-2. Any loss that could happen to this data may negatively affect the organization’s confidence and might damage their reputation. Google Scholar. Variety: the category of data and its characteristics. The performance factors considered in the simulations are bandwidth overhead, processing time, and data classification detection success. For example, the IP networking traffic header contains a Type of Service (ToS) field, which gives a hint on the type of data (real-time data, video-audio data, file data, etc.). This article examines privacy and security in the big data paradigm through proposing a model for privacy and security in the big data age and a classification of big data-driven privacy and security. All four generations -- millennials, Gen Xers, baby boomers and traditionalists -- share a lack of trust in certain institutions. In the proposed GMPLS/MPLS implementation, this overhead does not apply because traffic separation is achieved automatically by the use of MPLS VPN capability, and therefore our solution performs better in this regard. Data security is the practice of keeping data protected from corruption and unauthorized access. This kind of data accumulation helps improve customer care service in many ways. This Cloud Security Alliance (CSA) document lists out, in detail, the best practices that should be followed by big data service providers to fortify Editor-in-Chief: Zoran Obradovic, PhD. Confidentiality: the confidentiality factor is related to whether the data should be encrypted or not. Big Data. But it’s also crucial to look for solutions where real security data can be analyzed to drive improvements. However, the algorithm uses a controlling feedback for updating. The invention of online social networks, smart phones, fine tuning of ubiquitous computing and many other technological advancements have led to the generation of multiple petabytes of both structured, unstructured and … International Journal of Production Re search 47(7), 1733 –1751 (2009) 22. Function for distributing the labeled traffic for the designated data_node(s) with. Indeed, our work is different from others in considering the network core as a part of the big data classification process. In addition, the simulated network data size ranges from 100 M bytes to 2000 M bytes. Another work that targets real-time content is presented in [10], in which a semantic-based video organizing platform is proposed to search videos in big data volumes. Automated data collection is increasing the exposure of companies to data loss. So instead of giving generic advice about “security,” I want to show you some ways you can secure yourself and … Moreover, Tier 2 is responsible for evaluating the incoming traffic according to the Velocity, Volume, and Variety factors. The research on big data has so far focused on the enhancement of data handling and performance. At this stage, Tier 2 takes care of the analysis and processing of the incoming labeled big data traffic which has already been screened by Tier 1. Big data is the collection of large and complex data sets that are difficult to process using on-hand database management tools or traditional data processing applications. Every generation trusts online retailers and social networking websites or applications the least with the security of their data, with only 4% of millennials reporting they have a lot of trust in the latter. Our assumption here is the availability of an underlying network core that supports data labeling. (2018). 1 journal in Big data research with IF 8.51 for 2017 metric. The rest of the paper is organized as follows. (ii)Treatment and conversion: this process is used for the management and integration of data collected from different sources to achieve useful presentation, maintenance, and reuse of data. It is the procedure of verifying information are accessible just to the individuals who need to utilize it for a legitimate purpose. Thus, the treatment of these different sources of information should not be the same. (ii)Tier 1 is responsible to filter incoming data by deciding on whether it is structured or nonstructured. Security Issues. The proposed method is based on classifying big data into two tiers (i.e., Tier 1 and Tier 2). Loshima Lohi, Greeshma K V, 2015, Big Data and Security, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) NSDMCC – 2015 (Volume 4 – Issue 06), Open Access ; Article Download / Views: 27. We are committed to sharing findings related to COVID-19 as quickly as possible. In addition, the. In related work [6], its authors considered the security awareness of big data in the context of cloud networks with a focus on distributed cloud storages via STorage-as-a-Service (STaaS). The increasing trend of using information resources and the advances of data processing tools lead to extend usage of big data. 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