An Overview of Security and Privacy Issues in Big Data

Big data refers to a very large volume of data with possibly varied and complex structure. With growing data processing and data analytic techniques, big data provides significant benefits to organisations and individuals by improving productivity and enriching people's life. However, security and privacy are big concerns for big data applications. While a large quantity of data is collected, securely storing, processing and using the data are challenging. In this paper, we propose a four-factor framework for big data security and privacy in business information systems. The proposed framework addresses big data security and privacy issues in terms of collecting the right data, collecting the right amount of data, protecting the data in the right way, and using the data for the right purposes. We present a set of approaches and models for each of the four factors to improve big data security and privacy.

Due to the reasons such as the rapid growth and spread of network services, mobile devices, and online users on the Internet leading to a remarkable increase in the amount of data. Almost every industry is trying to cope with this huge data. Big data phenomenon has begun to gain importance. However, it is not only very difficult to store big data and analyse them with traditional applications, but also it has challenging privacy and security problems. For this reason, this paper discusses the big data, its ecosystem, concerns on big data and presents comparative view of big data privacy and security approaches in literature in terms of infrastructure, application, and data. By grouping these applications an overall perspective of security and privacy issues in big data is suggested.

The ACM 1st International Workshop on Privacy and Security of Big Data (PSBD 2014), held in Shanghai, China on November 7, 2014, in conjunction with the ACM 23rd International Conference on Information and Knowledge Management (CIKM 2014), presents research on privacy and security of big data, an emerging challenge in actual database and data mining research. PSBD 2014 program has two interesting s/essions on (i) scalable privacy-preserving and security-control methods for big data processing, and (ii) user-oriented and data-oriented privacy methods for big data processing, plus a panel discussing current challenges and future research perspectives of privacy and security of big data.

Security and privacy issues are magnified by the volume, variety, and velocity of Big Data, such as Large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition and high volume inter-cloud migration. In the past, Big Data was limited to very large organizations such as governments and large enterprises that could afford to create and own the infrastructure necessary for hosting and mining large amounts of data. These infrastructures were typically proprietary and were isolated from general networks. Today, Big Data is cheaply and easily accessible to organizations large and small through public cloud infrastructure. The purpose of this chapter is to highlight the Big Data security and privacy challenges and also presents some solutions for these challenges, but it does not provide a definitive solution for the problem. It rather points to some directions and technologies that might contribute to solve some of the most relevant and challenging Big Data security and privacy issues.

To study and solve the issues of big data security and privacy protection is the key to promote the healthy development of big data. In this paper, a 3D big data security and privacy protection model based on life cycle, security goal and user role is presented, and the security challenges and security goals of big data are analyzed from different aspects. The feasible technology and its application in big data security and privacy protection are expounded and analyzed, and the in-depth research direction of big data security and privacy protection is discussed and summarized.

Privacy and security of Big Data is gaining momentum in the research community, also due to emerging technologies like Cloud Computing, analytics engines and social networks. In response of this novel research challenge, several privacy and security of big data models, techniques and algorithms have been proposed recently, mostly adhering to algorithmic paradigms or model-oriented paradigms. Following this major trend, in this paper we provide an overview of state-of-the-art research issues and achievements in the field of privacy and security of big data, by highlighting open problems and actual research trends, and drawing novel research directions in this field.

Due to the proliferation of the Internet, IoT, and Cloud Computing, there is now an abundance of virtual data in every industry, field of study, and government agency. Big data has quickly become a topic of intense interest, garnering media coverage and commentary from all over the world. Data privacy and security in Big Data is a pressing concern. The 5Vs of big data—size, velocity, value, veracity, and variety—lower the bar for adequate protection. This paper aimed to draw attention to security and privacy issues and challenges associated with Big Data in healthcare, the resolution of which could result in a more secure data processing and computing infrastructure. This paper also provides a high-level overview of the K-Anonymity technique for protecting the privacy of large datasets before they are released for analysis, with the goal of preventing the disclosure of personally identifiable information. In conclusion, this paper summarizes the functions and features of the best big data security solutions offered by industry leaders.

Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data. This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.

The growing adoption of big data technologies in healthcare has significantly enhanced patient care, diagnostics, and system efficiency. Yet, this transformation brings with it serious concerns about the security and privacy of sensitive medical information. The health care industry is in a critical phase of intelligence with the rapid advancement of modern information technology. With the increased use of healthcare big data, the issue of information security is increasingly becoming critical in the management of smart healthcare care, including the leak of patient privacy, the most critical issue. Thus, the enhancement of information management of intelligent health care during the age of big data is a significant aspect of long-term sustainable development of the hospitals. This paper has initially determined the most influential indicators to influence the privacy disclosure of big data in managing health care and presented a comprehensive overview of the current landscape in healthcare data security and thereafter set the privacy and security based access control model, which has been applied on the security and management of big data in utilization of medical data, and solves the issue of actual data breach where actual problems are involved. Lastly, the model is contrasted with the state-of-the-art techniques. The paper offers a comparative analysis of proposed solution considering numerous parameters and highlights critical gaps for building more secure and trustworthy healthcare systems. The findings confirm that the model is useful in the evaluation of the existing safety threats and forecasting the scope of the various risk factors, by demonstrating that Network Segmentation and Cloud Usage (Hybrid) have significantly enhanced the results by 1.5% and 6.7% respectively and the User Access mechanism is upgraded by 1.5% and 6.7% respectively. The Audit Trail and Compliance is also improving as the proposed technique examines ACL and explores key global regulatory frameworks like the GDPR and HIPAA. The outcome of this study suggests that the proposed access control model is resistant to most cyber-attacks in big data, and it is also demonstrated that the offered framework can be used as a starting point in order to develop secure and safe medical big data solutions. Therefore, this study can be valuable to future scholars to understand the information about the security and privacy of big data in the medical field and ways to implement countermeasures.

In view of the wide application and popularization of large data, more and more data security and privacy issues have brought great challenges to the development of large data. Starting from the characteristics of big data, this paper analyses various risks of information security, and puts forward the corresponding development strategy of big data security. The results show that the combination of technology and relevant policies and regulations can better solve the problem of big data security and privacy protection.

Big Data is a very huge volume of data which is beyond the storage capacity and processing capability of traditional system. The volume of data is increasing at exponential rate. Therefore, there is the need of such mechanism to store and process such high volume of data. The impressiveness of the Big data lies with its major applicability to almost all industries. Therefore, it represents both, the tremendous opportunities and complex challenges. Such omnipotent eminence leads to the privacy and security related challenges to the big data. Nowadays, security of big data is mainly focused by every organization because it contains a lot of sensitive data and useful information for taking decisions. The hostile nature of digital data itself has certain inherited security challenges. The aim of Big data security is to identify security issues and to find the better solution for handling security challenges. The observation and analysis of different security mechanism related to the issues of big data and their solutions are focused in this chapter.

A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.

The "Big Data" era brings massive data inflows from diverse sources, creating opportunities and privacy/security challenges. With rising public concern due to frequent data breaches, protecting privacy and data security has become crucial. This paper summarizes current academic research and developments in data protection technologies, including encryption, anonymization, and access control. It aims to provide insights into data security and privacy in big data, examining future technological prospects and challenges.

Social networking big data: Opportunities, solutions, and challenges

IT기술의 발전은 기존의 컴퓨터 환경과 더불어 수많은 모바일 환경 및 사물 인터넷환경을 통해 사람의 삶을 편리하게 하고 있다. 이러한 모바일과 인터넷 환경의 등장으로 데이터가 급속히 폭증하고 있으며, 이러한 환경에서 데이터를 경제적인 자산으로 활용 가능한 Big Data 환경과 서비스가 등장하고 있다. 그러나 Big Data를 활용한 서비스는 증가하고 있지만, 이러한 서비스를 위해 발생되는 다량의 데이터에는 보안적 문제점이 있음에도 불구하고 Big Data의 보안성에 대한 논의는 미흡한 실정이다. 그리고 기존의 Big Data에 대한 보안적인 측면의 연구들은 Big Data의 보안이 아닌 Big Data를 활용한 서비스의 보안이 주를 이루고 있다. 이에 따라서 본 연구에서는 Big Data의 서비스 산업의 활성화를 위하여 Big Data의 보안에 대한 연구를 하였다. 세부적으로 AHP 기법을 활용한 Big Data 환경에서 보안관리를 위한 구성요소를 파악하고 그에 대한 우선순위를 도출하였다. The existing computer environment, numerous mobile environments and the internet environment make human life easier through the development of IT technology. With the emergence of the mobile and internet environment, data is getting bigger rapidly. From this environment, we can take advantage of using those data as economic assets for organizations which make dreams come true for the emerging Big Data environment and Big Data security services. Nowadays, Big Data services are increasing. However, these Big Data services about Big Data security is insufficient at present. In terms of Big Data security the number of security by Big Data studies are increasing which creates value for Security by Big Data not Security for Big Data. Accordingly in this paper our research will show how security for Big Data can vitalize Big Data service for organizations. In details, this paper derives the priorities of the components of Big Data Information Security Service by AHP.