In contrast, the authors in [12] focused on the big data multimedia content problem within a cloud system. It is also worth noting that analyzing big data information can help in various fields such as healthcare, education, finance, and national security. Having reliable data transfer, availability, and fast recovery from failures are considered important protection requirements and thus improve the security. Hill K. How target figured out a teen girl … The journal will accept papers on … While opportunities exist with Big Data, the data can overwhelm traditional We also simulated in Figure 9 the effectiveness of our method in detecting IP spoofing attacks for variable packet sizes that range from 80 bytes (e.g., for VoIP packets) to 1000 bytes (e.g., for documents packet types). In addition, authentication deals with user authentication and a Certification Authority (CA). So far, the node architecture that is used for processing and classifying big data information is presented. Online Now. Transparency is the key to letting us harness the power of big data while addressing its security and privacy challenges. 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 … The rest of the paper is organized as follows. Data security is a hot-button issue right now, and for a good reason. Variety: the category of data and its characteristics. Therefore, attacks such as IP spoofing and Denial of Service (DoS) can efficiently be prevented. 53 Amoore , L , “ Data derivatives: On the emergence of a security risk calculus for our times ” ( 2011 ) 28 ( 6 ) Theory, Culture & Society 24 . Authors in [2] propose an attribute selection technique that protects important big data. We are committed to sharing findings related to COVID-19 as quickly as possible. 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. (v)Analyzing and processing big data at Networks Gateways that help in load distribution of big data traffic and improve the performance of big data analysis and processing procedures. The authors in [4] developed a new security model for accessing distributed big data content within cloud networks. ISSN: 2167-6461 Online ISSN: 2167-647X Published Bimonthly Current Volume: 8. Performs header and label information checking: Assumptions: secured data comes with extra header size such as ESP header, (i) Data Source and Destination (DSD) information are used and. Big data security and privacy are potential challenges in cloud computing environment as the growing usage of big data leads to new data threats, particularly when dealing with sensitive and critical data such as trade secrets, personal and financial information. Other security factors such as Denial of Service (DoS) protection and Access Control List (ACL) usage will also be considered in the proposed algorithm. (iii)Tier 2 is responsible to process and analyze big data traffic based on Volume, Velocity, and Variety factors. Each node is also responsible for analyzing and processing its assigned big data traffic according to these factors. The initiative aims at exploring proper and efficient ways to use big data in solving problems and threats facing the nation, government, and enterprise. Vulnerability to fake data generation 2. IJCR is following an instant policy on rejection those received papers with plagiarism rate of more than 20%. Big data innovations do advance, yet their security highlights are as yet disregarded since it’s trusted that security will be allowed on the application level. Moreover, the work in [13] focused on the privacy problem and proposed a data encryption method called Dynamic Data Encryption Strategy (D2ES). (ii)Using of data-carrying technique, Multiprotocol Label Switching (MPLS) to achieve high-performance telecommunication networks. Chief Scientific Officer and Head of a Research Group Finally, in Section 5, conclusions and future work are provided. 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. 32. 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 approach as will be shown later on in this paper helps in load distribution for big data traffic, and hence it improves the performance of the analysis and processing steps. The main improvement of our proposed work is the use of high speed networking protocol (i.e., GMPLS/MPLS) as an underlying infrastructure that can be used by processing node(s) at network edges to classify big data traffic. Copyright © 2018 Sahel Alouneh et al. Our proposed method has more success time compared to those when no labeling is used. The proposed architecture supports security features that are inherited from the GMPLS/MPLS architecture, which are presented below: Traffic Separation. Abouelmehdi, Karim and Beni-Hessane, Abderrahim and Khaloufi, Hayat, 2018, Big healthcare data: preserving security and privacy, Journal of Big Data, volume 5,number 1, pages 1, 09-Jan 2018. Keywords: Big data, health, information, privacy, security . Forget big brother - big sister's arrived. 12 Big data are usually analyzed in batch mode, but increasingly, tools are becoming available for real-time analysis. 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. Mon, Jun 2nd 2014. All rights reserved, IJCR is following an instant policy on rejection those received papers with plagiarism rate of. Furthermore, honestly, this isn’t a lot of a smart move. In today’s era of IT world, Big Data is a new curve and a current buzz word now. This factor is used as a prescanning stage in this algorithm, but it is not a decisive factor. However, Virtual Private Networks (VPNs) capabilities can be supported because of the use of GMPLS/MPLS infrastructure. The first algorithm (Algorithm 1) decides on the security analysis and processing based on the Volume factor, whereas the second algorithm (Algorithm 2) is concerned with Velocity and Variety factors. Big data security and privacy are potential challenges in cloud computing environment as the growing usage of big data leads to new data threats, particularly when dealing with sensitive and critical data such as trade secrets, personal and financial information. The core network consists of provider routers called here P routers and numbered A, B, etc. The two-tier approach is used to filter incoming data in two stages before any further analysis. Moreover, moving big data within different clouds that have different levels of sensitivity might expose important data to threats. Big data security analysis and processing based on volume. Although bringing AI into big data processing could comprehensively enhance service quality, the issues of security, privacy and trust remain a challenge due to the high possibility of a data breach during the multimedia compression, transmission and analysis. Reliability and Availability. Kim, and T.-M. Chung, “Attribute relationship evaluation methodology for big data security,” in, J. Zhao, L. Wang, J. Tao et al., “A security framework in G-Hadoop for big data computing across distributed cloud data centres,”, G. Lafuente, “The big data security challenge,”, K. Gai, M. Qiu, and H. Zhao, “Security-Aware Efficient Mass Distributed Storage Approach for Cloud Systems in Big Data,” in, C. Liu, C. Yang, X. Zhang, and J. Chen, “External integrity verification for outsourced big data in cloud and IoT: a big picture,”, A. Claudia and T. Blanke, “The (Big) Data-security assemblage: Knowledge and critique,”, V. Chang and M. Ramachandran, “Towards Achieving Data Security with the Cloud Computing Adoption Framework,”, Z. Xu, Y. Liu, L. Mei, C. Hu, and L. Chen, “Semantic based representing and organizing surveillance big data using video structural description technology,”, D. Puthal, S. Nepal, R. Ranjan, and J. Chen, “A Dynamic Key Length Based Approach for Real-Time Security Verification of Big Sensing Data Stream,” in, Y. Li, K. Gai, Z. Ming, H. Zhao, and M. Qiu, “Intercrossed access controls for secure financial services on multimedia big data in cloud systems,”, K. Gai, M. Qiu, H. Zhao, and J. Xiong, “Privacy-Aware Adaptive Data Encryption Strategy of Big Data in Cloud Computing,” in, V. Chang, Y.-H. Kuo, and M. Ramachandran, “Cloud computing adoption framework: A security framework for business clouds,”, H. Liang and K. Gai, “Internet-Based Anti-Counterfeiting Pattern with Using Big Data in China,”, Z. Yan, W. Ding, X. Yu, H. Zhu, and R. H. Deng, “Deduplication on Encrypted Big Data in Cloud,” in, A. Gholami and E. Laure, “Big Data Security and Privacy Issues in the Coud,”, Y. Li, K. Gai, L. Qiu, M. Qiu, and H. Zhao, “Intelligent cryptography approach for secure distributed big data storage in cloud computing,”, A. Narayanan, J. Huey, and E. W. Felten, “A Precautionary Approach to Big Data Privacy,” in, S. Kang, B. Veeravalli, and K. M. M. Aung, “A Security-Aware Data Placement Mechanism for Big Data Cloud Storage Systems,” in, J. Domingo-Ferrer and J. Soria-Comas, “Anonymization in the Time of Big Data,” in, Y.-S. Jeong and S.-S. Shin, “An efficient authentication scheme to protect user privacy in seamless big data services,”, R. F. Babiceanu and R. Seker, “Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook,”, Z. Xu, Z. Wu, Z. Li et al., “High Fidelity Data Reduction for Big Data Security Dependency Analyses,” in, S. Alouneh, S. Abed, M. Kharbutli, and B. J. Mohd, “MPLS technology in wireless networks,”, S. Alouneh, A. Agarwal, and A. En-Nouaary, “A novel path protection scheme for MPLS networks using multi-path routing,”. This special issue aims to identify the emerged security and privacy challenges in diverse domains (e.g., finance, medical, and public organizations) for the big data. The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The proposed security framework focuses on securing autonomous data content and is developed in the G-Hadoop distributed computing environment. The network core labels are used to help tier node(s) to decide on the type and category of processed data. Review articles are excluded from this waiver policy. Therefore, this research aims at exploring and investigating big data security and privacy threats and proposes twofold approach for big data classification and security to minimize data threats and implements security controls during data exchange. International Journal of Production Re search 47(7), 1733 –1751 (2009) 22. Big Data. Daily tremendous amount of digital data is being produced. However, it does not support or tackle the issue of data classification; i.e., it does not discuss handling different data types such as images, regular documents, tables, and real-time information (e.g., VoIP communications). In [8], they proposed to handle big data security in two parts. This in return implies that the entire big data pipeline needs to be revisited with security and privacy in mind. In Section 3, the proposed approach for big data security using classification and analysis is introduced. A big–data security mechanism based on fully homomorphic encryption using cubic spline curve public key cryptography. Therefore, a big data security event monitoring system model has been proposed which consists of four modules: data collection, integration, analysis, and interpretation [ 41 ]. Indeed, It has been discussed earlier how traffic labeling is used to classify traffic. Function for distributing the labeled traffic for the designated data_node(s) with. Therefore, security implementation on big data information is applied at network edges (e.g., network gateways and the big data processing nodes). Handling approach was proposed big data security journal big data and its characteristics labeling on the use labels. And varied encryption techniques on fully homomorphic encryption using cubic spline curve public key cryptography research articles well. And fast recovery from node or link failures fast and efficient helps in communicating data clearly efficiently. 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