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Information Technology has seeped into every sector of industry, financial institutions and commercial organizations.

The use of computers and their associated technologies like Internet, Networking, Cloud computing, Grid Computing, Virtualization and Big data has made the world a better global village with their speed, ease, connivance, capabilities and low cost features.However, the primary concerns in today’s world of information technology are of privacy and security of the information that is stored in the computing resources like systems, servers, websites, networks or devices.

Research project study paper has been undertaken with the primary goal of identifying the privacy and security issues that are associated with implementing Big Data technology in a commercial environment.


What are privacy and security issues?

What is big data?

What is the Data collection method and Data analyses method ?

What are privacy and security issues?

What are privacy and security issues?

A typical privacy or security attack is deliberately done to cause potential harm to an individual user from privacy and security issues really boils down to how much the value of information is available online, as well as the amount of information that can be accessed through online resources. In other words, the hackers or crackers generally target the persons who have more valuable or useable data than the others in the concerned system, server, network or website [4]. For example a Facebook user with more than 1000 friends and membership in more than 100 groups are more likely to be targeted for conducting hacking than someone who barely uses the site and has few friends. In final words, a privacy or security issues fundamentally occurs based on the value of data stored and the target victims are chosen on their value basis.

Most of the privacy and security lapses necessarily involve the exploitation of a individual user’s private information. Usually the targeted victims are the users who have access to valuable data storage like Administrators, Managers, and Security personnel. 95% of all hacking or phreaking incidents are caused by social engineering methodologies and expert exploit programmers cause the rest of 5% of the security breach incidents. Hackers entice their victims with user participation invitations, prize winning messages, invitations, sales deals, photos, open platform applications, etc. —to gain access to their private information with special focus on their professional information. Hence in order to resolve these privacy and security issues educating the users is the best viable solutions to prevent the incidents of hacking, data theft, or the malicious activities of a nefarious application developer.

The technical term “Big Data” defines a very large volume of cloud-based storage consisting of all types of data elements like structured, semi-structured and unstructured elements [8]. In today’s world of information technology computers are used in every sector of industry, financial institutions and commercial organization for using their features of low-cost, high processing speed, large storage volume and versatile program capability. This has resulted in unprecedented phenomenally exponentially growth the computer-based storages with time on a day-to-day basis as the enterprises conducted their daily business activities. Such huge data sets cannot be handled by ordinary databases or data warehouses that exist today, as their technology does not support any assorted data sets [6].

What is big data?

Hence all enterprise-level business organizations conduct their day-to-day basis regular existential work on Big data which can easily support these  extremely large datasets of assorted data types by arranging them in a specific way that the data sets may be data mined to analyze to accurately reveal interesting and useful business intelligence like patterns, trends, spikes, surges, and associations [10]. This allows the enterprise levels companies, social networks, popular websites and government IT institutions to better understand their present scenarios and plan for their future activities based on the acquired business intelligence.

Significance of the research

Mentioned in detail below are the four primary features of Big Data:

  1. Support for large storage volume up to Zeta bytes size
  2. Support to huge variety of data sets including structured, semi-structured and unstructured data sets,
  3. High speed of processing and communication and
  4. Extensive output information variability.

As any complex system will have, inbuilt vulnerabilities will always have existing intricate structures built into it [9]. It is just common to have vulnerabilities in Big Data too, due to the intricate level of complexity in its technology regarding its novel and innovative storage methodology and data access process. As most governments, enterprise levels companies, social networks, popular web sites, technological companies maintain and process valuable information, or provide services to multiple users concurrently using Big Data technology, it is primarily necessary to provide security safeguards against unauthorized access, use, or modifications of any file or computing resource[14]. Privacy and security of data is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a security framework to protect Big Data based websites, storage spaces, social networks is a very immediate hot topic research. Hence I have chosen to conduct my final year research study on this topic.

In present time and in the future, concerns for privacy and security are the foremost concern of every individual, commercial organization, financial institution and governments as they are all dependent on the use of information technology and computer science to execute their day to day activities.  The only solution to resolve this is to make security and privacy polices mandatorily integral in the planning and design of computer systems and their applications [7]. This is very difficult problem to tackle and it has not yet been solved in the general case. In order to prevent privacy and security issues there must be end to end security in all stages of data life cycle starting from data generation, data storage, data modification, data communication, and data archival to the final stage of data deletion [8]. This involves the below steps right from data creation stage at Computer systems. Computers, servers, networks and web sites are the main sources of data generation and must be protected against unauthorized use, malicious attacks, compromise, disruption of operations, and physical damage [10].

The Data Collection and Analysis Methods

The growing number of computer based technologies and applications involving valuable information or assets are directly proportional to the growing number of criminal actions directed against these computer applications and systems or perpetrated by using computers. These criminal incidents underscore the need for finding effective solutions to the Big Data privacy and security problem [4]. This research study revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably identifiable privacy and security aspects required by Big Data applications [3]. My research topic of providing privacy and security to Big data environment is addressed in two sections of the along with their associated case studies.

Big data is a novel and innovative technologies with complex intricate structural environment consisting of other information technologies like Grid Computing, Cloud computing, Gigabit Network, Shared Pooling, Virtualization, In Memory processing, Parallel processing etc., Hence most of the traditional IT infrastructure security mechanisms such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data [14].

The ideal solution to resolve the privacy and security issues associated with Big data environments is the emergent SDN – software defined network [7]. SDN is a novel and innovative network management solution that has the potential to become a convenient mechanism to implement security in Big Data systems.  There is not much a technical discussion or relevant research work on my research topic of SDN which identifies open issues [6].

In today’s digital world the collection, analysis, and management of the acquired data is the core part of any commercial or business activity. Due to the increasing rate of computing resources usage there has been proportionately double the workload of data management over the Big data environment [15]. The complexity of managing, storing, processing and visualizing data in an Big Data environment is very vast and intricate. Hence, any inefficiency due to system or strategic decision gets affected to the misprocessing in retrieving data from the huge amount of data available on a Big Data environment [12]. Security and confidentiality of the big data environment is the major challenge in the scheduling and managing of the large volume of data available over the network [13]. The Big data environment can be clearly defined by its primary feature which is vast variety, large volume, big veracity, high velocity, and great value.

Significance of the Research

The aim of this paper is to focus on the security and the privacy issues, which exist with the arrangement of big data.

The objective of the research is to find out the privacy and security issues, which exist with the big data management plan. The study helps us to find out the possible solution for the management of big data [7].

Fig.1. Graphical abstract of the analysis: Security and Privacy Challenges of Big Data

For the literature review on my research topic “privacy and security with Big Data” I have pooled the data of the previously published research articles from 12 peer-reviewed scientific studies that were conducted in the previous two decades [5]. Much of the comparative data presented in this research study is derived from them and includes analysis results information, application methodology, security issues, privacy issues and algorithm development.

The pooled data of the previously published research articles amounting to 12 peer-reviewed scientific studies that were conducted in the previous two decades is deeply analyzed by categorizing the collected data and displaying emerging patterns in a diagram and table. The data sets include state description attributes such as volume of data, application area, and security issues that are prevalent in that area [12]. The research methodology used allows the resolution of the identified issues, as the specific algorithm that is selected, is particularly used to for this purpose only.

The focus should be given on the challenges which exist with the management of the big data. The solution of security dimensions should be explored for the retrieving of data on demand. The interviews, questionnaires, study of previous work done, and selection of the case study should be undertaken for the data collection for the study of security and privacy issues which exist with the management of big data [15].

  • Interview: The interview should be arranged with the professionals of the cloud environment who deals with the management of the big data within their organization. The research questions undertaken should be asked for getting relevant information regarding issues and challenges in the big data.
  • Questionnaire: The large sample of IT professionals should be taken to arrange the questionnaire to get the solution of the research question.
  • Previous work done: The study of the literature review helps in investigating the gaps which exist with the management of the big data.
  • Selection of the case study: The case study based on big data security should be undertaken for study. The abstracts of the research papers should be arranged in synchronised manner.

From the research study, we conclude following area of concern which should be taken for managing the big data analytics:

Area of concern

Description

Security of Hadoop file system

The security issues exist with the infrastructure of the Hadoop file system [16]. The authenticationand authorization mechanism is the major concern of Hadoop security system

Data availability

It is difficult in managing on demand supply of data from the large volume of available data. The complexity arises when same data is asked by multiple active node [14]

Security issues with the architecture

The modification and manipulation is required in the infrastructure of the Hadoop file system

Issues related with authentication

Encrypting the data is the major task for providing authentication to the data.

Communication flow

The flow of communication is the major problem in the deployment of Hadoop file system. The network protocols should be used for managing the big data

The following diagram shows the major area of concern in relation to the big data security:

The research area should be expanded for managing the privacy issues which exist with the management of big data over the cloud network. The confidentiality of the data is the major issues for securing the personal details of the user. The data leakage is the main area of concern for keeping the data private and confidential [12]. The confidentiality of the data can be maintained with the inclusion of cryptographic approaches, deployment of Anonymization model, providing access control to the authenticated party [9].  The following diagram shows the methods which can be incorporated for managing the privacy concern of big data.

Originality of the Approach

The following table shows the methods which can be used for resolving the security and privacy issues which are incorporated with the management of the big data.

Security and Privacy issues

Purpose

Methods

Security issues with Hadoop management

Securing the deployment of Hadoop file system

Maintenance of trust between user and data through encryption technique [15]

Security issues with the cloud network

Securing the data stored on the cloud network

Use of authentication protocol

Monitoring issues

Detecting anomalies and intrusion

Deployment of the malicious control software

Auditing issues

Storing the big data through auditing technique

Development of the hash tree [13]

Key management issues

Storing of authentic keysenabling sharing of data over the group

Generation of quantum theory

Anonymization

Preserving the privacy of data mining techniques

Deployment of top-down and bottom-up hashtree Anonymization [12]


The research work helps in providing the knowledge about the laws and policies of the government which should be deployed for managing the big data [15]. The parameters should be taken under consideration for measuring the privacy level of the data. The risks of the big data can be minimized by following the government rules and policies. The initialization of algorithms and cryptographic approaches helps in developing the robust security model for managing the privacy and security issues of big data [15].

In todays, information age, where data and its security is paramount to all commercial, non-commercial, public and governmental organizations, Big data is the only technological solution that provides all the vital 3 v’s features – vast storage capacity, very high speed data streaming capability, and a variety of data comprising  of structured data, semi structured data and unstructured data. My research study identifies reviews, evaluates and offers appropriate solutions to assure security and privacy of the data stored in Big data environment.

In the first research paper [1], the authors have discussed the importance of Big data and reviewed its security and privacy issues in great detail. Security and privacy of Big data is a complex issue when taking into account the different sources and different types of datasets that constitutes the Big Data. The scholars have specifically reviewed the security and privacy issues related to various sources of big data like healthcare, social media, IOT, mobile apps and social networks. The issues in each domain have been discussed in detail, the study also elaborates on the privacy and security concerns of various future domains of Big Data as they emerge, from time to time due to the ongoing technological advancement in this information age.  This study paper [1] has provided me with very valuable data pertaining to security and privacy issue of implementing Big Data in various data sets domains.  However it does not prescribe any resolutions to the detected issues and thus can be used only to gain academic knowledge and not functional knowledge.

The Challenges

The authors of the second research study paper [2] have discussed in details all the types of security and privacy issues affecting Big Data due to its unique technology and vast environment footprint. The authors also discuss the steps taken by DARPA, NIST, OWASP, ISOC, MUSE and other associated technology governing bodies on the topic of Big data security and privacy issues. The study elaborates on the various frameworks, procedures and policies devised and framed by these organizations to resolve the issues of Big Data security and privacy.The complexity of implementing security and privacy when using the innovative technologies like  IOT, BYOD, Cloud Computing, JS, etc., increase with the extent of their usage. However the authors[2] have correctly segmented the security and privacy issues of Big data technology implementation into four major divisions namely as 1) Infrastructure security (can be resolved by implementing secure distributed computations using Map Reduce), 2) Data privacy (can be resolved by using implementing authorised data mining that preserves privacy by granular access authorization, 3) data security (can be resolved by implementing secure data provenance and storage) and 4) Integrity and reactive security (can be resolved by implementing  real time monitoring of anomalies and attacks using IDS and IPS device). Also the study does not discuss about the true challenges on maintaining end to end data security and privacy in a big data environment from the stage of ‘data creation’ to the stage of ‘data usage’.

The research scholars of the third study paper [3] reviewed the several security and privacy issues of Big data in the perspective of cloud computing platform. The authors described the several advantageous features of Big data, cloud computing, virtualization and DevOps technologies along with their associated security and privacy issue concerns. The authors list the identified security and privacy factors, that affect the activities of cloud based service providers and the legal processioning of consumer data in the context of genomic sequencing. Although the study paper [3] also discusses the various security and privacy solutions like Homomorphic encryption, Anonymization, SAIL, ScaBIA, RBAC, PKI. DPD, Bio Bank Cloud etc., it does not discuss the other aspects of Big data security and privacy and thus lacks holistic approach to resolve the security and privacy concerns of Big data.

The concerned research article [4] discusses the various benefits of using Big data technology to resolve the over whelming information crisis in today’s information age. However this study is concentrated on providing personal privacy in the domain of big data platform. The authors of the study elucidate four aspects to resolve the problem of protecting personal privacy. The authors state their opinion that only a combination of the technical means and legal means can resolve the problem of security and personal privacy of a data user in Big data environment. They propose four innovative methods for ensuring personal and data privacy in a big data environment, which are 1) Implementing anonymity protection by implementing user identity anonymity, attributes anonymity and relationship anonymity, 2) Implementing digital watermarking technology by embedding imperceptible watermarks within the data carrier, database and text files 3) Implementing Data Provence labelling technology to accurately determine the source of the data in the data warehouse. 4) Implement Role-based access control (RBAC) to permissions set, to restrict unauthorized data access and usage. The study [4] provides valuable policy based security and privacy protection techniques to my research but the authors fail to recommend any uniform policy design structure for implementing data security and personal privacy. However it focuses on a single aspect of personal privacy security of all the various security and privacy issues facing Big data environment implementation.

In the fifth paper [5], the authors have collected adequate literature data on successful implementation of privacy preservation methods in Big Data platform. They first discuss about the numerous advantages and drawbacks of implementing big data along with the various presented privacy and security issues in each phase of big data lifecycle in the context of big healthcare domain. The study stresses on using encryption, anonymization methods, Attribute based encryption algorithm, Access control, Homomorphic encryption, Storage path encryption and enhanced biometric devices. However the study [5] fails to provide any future direction or enhancement perspectives on the context of achieving effective solutions in privacy and security in the era of big data based healthcare data. Hence I suggest a detailed further study based on the present study of protecting big data based health care data.

In the sixth study paper [6] the research scholars have initially detailed the various advantages and disadvantages of implementing Big Data platform in citizen health care domain. The authors propose to develop a holistic strategy to protect and manage the sensitive health care data. In today’s World, most companies are developing advanced state of art business intelligence extraction techniques and decision-making intelligence capabilities. However in Big data it is a complex challenge due to its inherent extremely large data volumes and high speed streaming velocities and wide variety of structured, semi structured and unstructured data formats. However the authors [6] fail to provide a holistic strategy to resolve the be financial, personal, and other types of data issues but their report will helped me to clearly understand how to control and  protect sensitive information in the era of big data augmented information age.

In the seventh article [7], Advantech a hardware vendor company elaborates on the concerns about the security of Big Data. To resolve the security and privacy issues they suggest a software and hardware encryption technology that operates on live selected data or on an entire data warehouse, or on data at rest. But the challenge is implementing software-based encryption adds a significant extra load on a database server’s CPU and hence increases the operational costs, along with operational complexity. To resolve this issue Advantech recommends the usage of Intel Distribution for Apache Hadoop software for providing an enterprise ready Big Data analytics platform which is highly optimized for performance, stability, manageability, and security. This product accelerates the data encryption process by up to 5.3 x and data decryption by up to 19.8x thus providing a high-speed performance and better security. Although the proposed Advantech platforms [7] based on Intel Architecture for Apache Hadoop processing, analytics and communications Infrastructure provide a greater configurability, scalability and performance it is not a holistic approach and focuses mainly on a hardware solution. An ideal solution must contain both a hardware and software component to provide security and privacy for the data and the user.

In the eight research paper [8] the research scholars study various literature articles on the topic of providing security and privacy in Big Data platform. They clearly admit that it is impossible in the present day scenario to design and implement a single perfect data management solution for the cloud computing platform of Big Data as each security systems protects only a single aspect in the issue, and hence multiple open issue will remain. Hence they propose a three step method to resolve the issue starting with the first step of characterizing the different consistency semantics such that they can be provided at different scales, secondly implement effective techniques for load balancing and lastly design scalable, elastic, and autonomic multitenant database systems. Although the study paper [8] provides valuable data on the best methods to resolve security and privacy concern in big data environment for my research study it fails to provide any policy based strategy to resolve the concerns and hence it does not adequately resolve the issue of security and privacy of big data

In the ninth research paper [9], the authors suggest an Open SDRM (Open Source Digital Rights Management) system that allows content producers and providers to create their own customized business model at a very attractive price using a set of adaptive and standardized components. The framework for this standard is being developed by MOSES EC RTD project. As the existing DRM schemes base their security on proprietary protection methods, the authors propose an open source approach for Digital Rights Management by designing OSDRM platform. OSDRM provides open-source technology and standards on an integrated architecture to enable content producers to create custom protective measures for their data. This research study paper [9] provides a very innovative and holistic approach to resolve the security and privacy concerns in Big data but does not provide solutions to the other aspects of security and privacy of big data. However the solution is novel that uses existing open source technology to resolve Big data security and privacy issues.

In the tenth research paper [10], the researchers have proposed the implementation of a CTSMC model of an intrusion tolerant system to be used with a dual mode of switching time from an automatic detection mode to a manual detection mode. The appropriate switching time has been previously derived analytically using a statistical estimation algorithm of 50 to 100 parallel SITAR operations. Hence the optimal switching technique effectively improves the system availability/MTTSF. In the previous research studies, it has been clearly proved that the combination of intrusion tolerance architecture and a control of detection mode for intrusions were very effective to manage the critical computer-based systems. Although the research study paper [10] provides valuable data for my research study of an efficient technique to resolve security and privacy concerns in Big data environment, it does not provide any policy implementation on the human user side and hence may lack in security. 

Many different kinds of methods and methodologies were suggested in the previous research papers and literature I have studied but none of them provide a holistic approach to resolve the issue by preventing the occurrence of the issue. My research study focuses on preventing the occurrence of security issue by implementing a security strategy encompassing for the entire data strategy cycle of the Enterprise Company involving policies for the hardware, software, communication media, data owner and the data user. The previous research work has clearly and definitely identified the various challenges that have to be addressed to provide security and privacy to Big data environment. I have partaken some their data collection and methodologies and optimized them to a great extent to implement my holistic Design methodology approach of providing mandatory regulatory policies on the entire infrastructure of Big Data including the data owners and data users. However, none of the previous research studies have suggested this approach of using a hybrid approach of SDN, IDS and IPS and hence it is novel. I have conducted preliminary tests and found that my approach is successful in providing security and privacy in an Big data environment without hindering the performance levels of the hardware equipment, software resources and network media.

The present research work helps in studying the life cycle of data and framing a security management plan on the big data environment. The SDN protocols should be used for managing the big data [17]. The deployment of the researched solution helps in overcoming the problem of security and privacy which exist with the transmission flow of big data during the communication with the third party sources.

Although there are many different approaches to resolve the security and privacy issues associated with Big data but none of the twelve previous research papers studied provide a holistic all-inclusive solution. The concerned researchers provided solutions and suggestions only to the singular or few aspects of big data [8], which they dealt with in their research study. In my research study, I have proposed a comprehensive holistic approach to providing security and privacy covering the entire software, hardware, communication media, data owner and data user by implementing administrative and technical policies right from the first stage of data creation to the end stage of data analytics or data usage. Although there are many different and numerous aspects of outcomes needed to consider while addressing the issue of Big data security my research study focused on starting with the initial stage of data creation, the business case stage, the analytics stage, the stakeholders stage [15], and finally the data usage stage by the end user or customer. My holistic security approach addresses the entire data strategy cycle of the enterprise companies.

As Big data is a fairly new technology of the Information technology domain, hence it is obvious that enough research in the Big Data environment is not done and there exists the scope for further more research to be done [20]. In addition, there is an certain gap in the 12 scholarly articles mentioning the previously conducted research suggesting that most researchers were in bias towards traditional methods of providing privacy and security. Also, most of the scholarly articles research is one-sided, loopy and incomplete. In order to derive better conclusive results, a wider range of information needs to be considered in most articles. Hence, their results may not be accurate. Another point to consider is that there is lack of performance comparison of alternate solutions and how they could apply is another gap to resolve a different issue [13]. The previously conducted research literature chosen for this study also has many gaps in the area of resolution application that is how a certain security measure could be applied. In most of the literature articles, there is no case study related detailed information about solutions which may include algorithms that are used to resolve certain complex problems. In many literature articles, security issues are represented in a general form without stating any specific information related to its description or its causes, or its area of affection. In some particular literature articles citing the most common security and privacy issues, no mention of their details are included about what kind of information needs to be protected [21].

Every new technology resolves a major need of that time period but also comes with an intricate level of complexity and hence also consists of inbuilt vulnerabilities and threats which may pose a grave security danger and result in the breach of privacy of important information by making it public. Big data is a new and innovative technology area that is popularly referred by its synonym - the vast amount of data that needs to be stored, analyzed and mined in order to extract business intelligence [22]. However preventing confidentiality breaches, conducting regular integrity checks and provisioning the data and resources availability are the three mains concerns of a security administrator of the Big Data environment. Although there exists many insecurity issues in various areas of Big Data environment, sensitive information like private user authentication information needs to be protected at all costs [20]. Even though there is a large scope to conduct research on various aspects of Big data, as it is a new technology, more research is demanded primarily on the twin issues of privacy and security because it is the primary feature which every user and consumer is concerned about. Summary results of most literature clearly mention that security issues are similar in different areas of Big data and hence their resolutions can also be the same for those areas [17]. Another major point in the summary of most research literature is that many solutions are grounded on the usage of encryption algorithm. In summary, my research study has added valuable knowledge regarding the various big data security vulnerabilities and highlighted certain research gaps in those areas of Big Data [24]. However, Big Data is a vast technology and hence requires a continuing research covering all the emerging aspects of Big Data.

Conclusion

In conclusion, I have previously studied and deeply analyzed more than 12 peer-reviewed scientific publications of the past two decades related to my research topic of “privacy and security issues with Big Data”. This literature study has helped me to gather much research information on my research topic to evaluate the various possible solutions to resolve the privacy and security issues that arise in different areas of Big Data [19]. From this knowledge, I have identified a few certain gaps in the literature of previously conducted research related to my research topic. All of the previous research literature puts the onus of responsibility for providing privacy and security to the users’ data on the concerned company-provider, as they have the responsibility to ensure a safer infrastructure in compliance with confidentiality standards, protection of customer’s information, and secure data transmission. In every commercial business organization, resolving security issues is the key point for achieving effective and successful functioning of the company [23]. Although many different security technologies, protocols, policies and devices are been used to protect users and infrastructure in Big Data environment. The presently existing security technologies are not completely successful in completely resolving the concerned privacy and security issues. Although, previously many researchers have conducted various studies on privacy and security issues associated with Big Data [13], it is still a new computer science related technology; hence there certainly exists a lot of scope to conduct research on how to resolve the privacy and security problems existing in Big Data environment. In addition, there is a considerable lack of comparative analysis of both security issues in different areas of Big Data, so that we can derive appropriate solutions by comparing and contrasting the various previously stated solutions in these studies and finding resolutions for them [17]. Hence I have focused my research study with the aim of finding and comparing important security issues in different areas of Big Data and also evaluated solutions that can solve security issues.

References

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[24] International Congress on Big Data (pp. 25–            30). IEEE. doi:10.1109/ Big Data. Congress.2013.13HP.(2014)InternetofThingsResearcHStudy(p.4).Retrievedfromhttps://fortifyprotect.com                  /HP_IoT_Research_Study.pdf.

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