Security and Privacy in Big Data Computing

The rapid growth of digitization in the present era leads to an exponential increase of information which demands the need of a Big Data paradigm. Big Data denotes complex, unstructured, massive, heterogeneous type data. The Big Data is essential to the success in many applications; however, it has a major setback regarding security and privacy issues. These issues arise because the Big Data is scattered over a distributed system by various users. The security of Big Data relates to all the solutions and measures to prevent the data from threats and malicious activities. Privacy prevails when it comes to processing personal data, while security means protecting information assets from unauthorized access. The existence of cloud computing and cloud data storage have been predecessor and conciliator of emergence of Big Data computing. This article highlights open issues related to traditional techniques of Big Data privacy and security. Moreover, it also illustrates a comprehensive overview of possible security techniques and future directions addressing Big Data privacy and security issues.

The 1st International Workshop on Privacy and Security of Big Data (PSBD 2014) focuses the attention on privacy and security research issues in the context of Big Data, a vibrant and challenging research context which is playing a leading role in the Database research community. Indeed, while Big Data is gaining the attention from the research community, also driven by some relevant technological innovations (like Clouds) as well as novel paradigms (like social networks), the issues of privacy and security of Big Data represent a fundamental problem in this research context, due to the fact Big Data are typically published online for supporting knowledge management and fruition processes and, in addition to this, such data are usually handled by multiple owners, with possible secure multi-part computation issues. Some of the hot topics in the context privacy and security of Big Data include: (i) privacy and security of Big Data integration and exchange; (ii) privacy and security of Big Data in data-intensive Cloud computing; (iii) system architectures in support of privacy and security of Big Data, e.g., GPUs: (iv) privacy and security issues of Big Data querying and analysis. These topics are first-class aspects to be addressed and investigated by PSBD 2014. These proceedings contain the papers selected for presentation at the workshop. We received 12 submissions from countries in North America, Europe and Asia. After careful review, the program committee selected 5 papers for presentation at the workshop. The accepted papers were presented in 2 sessions: scalable privacy-preserving and security-control methods for Big Data processing, user-oriented and data-oriented privacy methods for Big Data processing. A panel discussed advanced aspects of privacy and security of Big Data. We hope that these proceedings will serve as a valuable reference for researchers and practitioners focusing on privacy and security of Big Data.

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.

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.

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.

Big data gradually become a hot topic of research and business and has been growing at exponential rate. It is a combination of structured, semi-structured & unstructured data which is generated constantly through various sources from different platforms like web servers, mobile devices, social network, private and public cloud etc. Big data is used in many organisations and enterprises, big data security and privacy have been increasingly concerned. However, there is a clear contradiction between the large data security and privacy and the widespread use of big data. In this paper, we have indicated challenges of security and privacy in big data. Then, we have presented some possible methods and techniques to ensure big data security and privacy.

With the ever-increasing cost for healthcare and increased health insurance premiums, there is a need for proactive healthcare and wellness. In addition, the new wave of digitizing medical records has seen a paradigm shift in the healthcare industry. As a result, the healthcare industry is witnessing an increase in sheer volume of data in terms of complexity, diversity and timeliness. As healthcare experts look for every possible way to lower costs while improving care process, delivery and management, big data emerges as a plausible solution with the promise to transform the healthcare industry. This paradigm shift from reactive to proactive healthcare can result in an overall decrease in healthcare costs and eventually lead to economic growth. While the healthcare industry harnesses the power of big data, security and privacy issues are at the focal point as emerging threats and vulnerabilities continue to grow. In this paper, we present the state-of-the-art security and privacy issues in big data as applied to healthcare industry.

Nowadays, many people get connected with each other in one virtual world known as "Cyber Society" instead of physically connected. The interaction of people with cyber society components, such as social media, search engines, blogs, websites - with their services, causes generation of enormous amount of data termed as, "Big Data". With adaption of Big Data in banking, finance, retail industry, health care, smart city, social media and IT sectors, it has started gaining importance along with many research challenges such as heterogeneity, data life cycle management, data processing, scalability, security and privacy, and data visualization. Many security and privacy issues emerged with Big Data that are not likely to be solved by conventional security solutions. Hence, this article is aimed to present overall perspective snapshot of security and privacy issues of Big Data.

Big Data is a field that provides different ways to analyze and to extract information and hidden patterns. It also helps to deal with the data sets which are complex and larger in size. In many cases, data offers greater statistical power while the data with higher complexity leads to a higher false discovery rate. At the current time due to the key concepts like volume, variety, and velocity which are associated with Big Data, privacy and security are the biggest challenges in this field. So in this chapter, we have discussed different types of issues and solutions related to security and privacy in Big Data. In the field of Big Data, privacy is the liberty to control how personal information is collected, organized, and used. Whereas security in Big Data refers to the process of protecting information from destructive forces and from the unwanted actions of unauthorized users, such as a cyberattack or a data breach. In this field, privacy and security both are very important issues. The security model of Big Data is not recommended for complex applications and as a result of which by default it gets disabled. But in the absence of this model, the data can be easily compromised. So through this chapter, we have tried to highlight the privacy threats, issues and challenges of Big Data. Several techniques required to maintain data security have also been covered in brief.

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.

BIG DATA is one of the emerging domain and performing a key role under several applications like, smart city, smart grid and intelligent transportation system, etc. In order to provide the real time services, Big Data has achieved a great success on the other side its counterpart is facing lot of challenges of privacy and security. Keeping in the view of real time access and services, security and privacy issues need to be given the highest priority. Personal and professional information's of an individual or an organization are globally available through cloud storage, which attract the attacker(s) and can be misused. Social media is very active and popular among the young generation, movement of very heavy traffic is involving under social environment, smart attackers capture the personal information from social environment and may create the problems against required information. As per the literature review number of proposals is available for the above mentioned issues but there is no guarantee of data privacy and security, therefore, still lot of scope is there for optimal or sub-optimal solutions. Motive of this study is to investigate the different challenges of big data for privacy and security through Indian prospective. We have collected the existing solutions for data privacy and security, and performed a comprehensive review by including their pros and cons. Impact areas of different cyber attacks have been investigated and covered in details. This study is not only preventing the data from fraud but also interested in knowing the culprit behind this fraud/attack.

Big data security and privacy in healthcare: A Review

The goal of this special issue is to collate a selection of representative research articles that were primarily presented at the 8th International Conference on Network and System Security (NSS 2014). This annual conference brings together researchers and practitioners in the world from both academia and industry who are working on network and system security, in order to foster interaction between researchers and developers, promote an exchange of ideas, discuss future collaborations, and develop new research directions. The recent advances in technology result in an explosive growth of data available. Sources such as sensors, social networks, Internet of Things, and the Internet are generating 2.5 quintillion bytes of data every day. With the vision of extracting useful knowledge and the promise of data-driven decision-making, big data have been emerging as a hot topic in the research community. While the benefits of big data are clear, it also introduces technical difficulties because of its scale, complexity, and heterogeneity. This special issue presents many examples of how researchers, scholars, vendors, and practitioners are collaborating to address security and privacy research challenges. The scope of this special issue is broad and is representative of the multi-disciplinary nature of security and privacy. In addition to submissions that deal with security challenges, privacy issues, theoretical analysis, algorithms, protocols, and practical experience in the context of big data, this issue also includes articles that address practical challenges with handling large-scale data at the infrastructure level. Addressing the privacy and security concerns in big data is a central topic of this issue. Chen et al. 1 describes how proxy re-encryption can be used to provide fine-grained access control in cloud storage, a popular way of storing data in people's daily life. Yu et al. 2 describe a new way to ensure the integrity of data outsourced on mobile cloud storage through a data integrity checking protocol. Hassan et al. 3 address the problem of efficient formation of cloud federation with the aim to support data-intensive applications. Ninggal et al. 4 highlight the privacy risk related to the disclosure of social network data with a new vertex re-identification attack. To protect real-world data across various platforms, key management is an essential issue. Qin et al. 5 propose two lightweight group key distribution schemes to solve the problem of efficient subgroup and intergroup communication in vehicular ad hoc networks. Biometrics has been gaining popularity, due to the increasing concerns of identity fraud and the need for identity recognition. Zhou et al. 6 improve the performance of fingerprint indexing through the combination of local and global reconstructed features. In the era of big data, resources are located in a heterogeneous network environment. Fang et al. 7 address the problem of secure routing and resource allocation in cooperative cognitive radio networks based on game theory. Yang et al. 8 provide a new design on handover authentication in the wireless mesh network through an improved key predistribution. We encourage the readers to review 9, 10 to gain insight into the latest innovations in the platform in the context of big data, such as fog computing 9, a new paradigm that extends cloud computing and services to the edge of the network, and private comparison in quantum cloud computing 10. We are grateful for the time and effort of the international reviewers. We deeply thank Professor Geoffrey C. Fox, the editor-in-chief, for providing this opportunity to publish this special issue. With his continuous support, encouragement, and guidance throughout this publishing project, this special issue has been very successful.

Chapter 12 - Security and Privacy in Big Data

Security in big data