Cloud-Centric Blockchain Public Key Infrastructure for Big Data Applications

A cloud-based public key infrastructure (PKI) utilizing blockchain technology is proposed. Big data ecosystems have scalable and resilient needs that current PKI cannot satisfy. Enhancements include using blockchains to establish persistent access to certificate data and certificate revocation lists, decoupling of data from certificate authority, and hosting it on a cloud provider to tap into its traffic security measures. Instead of holding data within the transaction data fields, certificate data and status were embedded into smart contracts. The tests revealed a significant performance increase over that of both traditional and the version that stored data within blocks. The proposed method reduced the mining data size, and lowered the mining time to 6.6% of the time used for the block data storage method. Also, the mining gas cost per certificate was consequently cut by 87%. In summary, completely decoupling the certificate authority portion of a PKI and storing certificate data inside smart contracts yields a sizable performance boost while decreasing the attack surface.

Security and Privacy Challenges in Big Data

Big data refers to enormous amount of information which may be in planned and unplanned form. The huge capacity of data creates impracticable situation to handle with conventional database and traditional software skills. Thousands of servers are needed for its processing purpose. Big data gathers and examines huge capacity of data from various resources to determine exceptional novel awareness and recognizing the technical and commercial circumstances. However, big data discloses the endeavor to several data safety threats. Various challenges are there to maintain the privacy and security in big data. Protection of confidential and susceptible data from attackers is a vital issue. Therefore, the goal of this chapter is to discuss how to maintain security in big data to keep your organization robust, operational, flexible, and high performance, preserving its digital transformation and obtaining the complete benefit of big data, which is safe and secure.

Big Data

This chapter gives information about the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by big data applications. Big data is one area where we can store, extract, and process a large amount of data. All these data are very often unstructured. Using big data, security functions are required to work over the heterogeneous composition of diverse hardware, operating systems, and network domains. A clearly defined security boundary like firewalls and demilitarized zones (DMZs), conventional security solutions, are not effective for big data as it expands with the help of public clouds. This chapter discusses the different concepts like characteristics, risks, life cycle, and data collection of big data, map reduce components, issues and challenges in big data, cloud secure alliance, approaches to solve security issues, introduction of cybercrime, YARN, and Hadoop components.

The Unheard Story of Organizational Motivations Towards User Privacy

There could be numerous reasons that drive organizations to provide privacy protections to end users in the applications they develop and maintain. Organizational motivations towards privacy affects the quality of privacy received by end users. Understanding these motivations and the approaches taken by organizations towards privacy protection would assist the policymakers and regulators to define effective frameworks encouraging organizational privacy practices. This study focuses on understanding the motivations behind organizational decisions and the approaches they take to embed privacy into the software applications. The authors analyzed 40 organizations different in size, scope, scale of operation, nature of data used, and revenue. they identified four groups of organizations characterized by the approach taken to provide privacy protection to their users. The taxonomy contributes to the organizational perspective of privacy. The knowledge presented here would help addressing the challenges in the domain of user privacy in software applications and services.

Enhance Data Security and Privacy in Cloud

With the advancement of communication and information technology, the internet has become used as a platform for computing and not only a way of communications networks. Accordingly, the large spread of cloud computing led to the emergence of different privacy implications and data security complexities. In order to enhance data security in the cloud, the authors propose in this chapter the use of an encryption box, which includes different cryptosystems. In fact, this step gives the user the opportunities to encrypt data with an unknown algorithm and makes a private key before the storage of data in the host company servers. Moreover, to manage the encryption database, the authors propose a quantum approach in search based on Grover's algorithm.

Security Vulnerabilities, Threats, and Attacks in IoT and Big Data

Big data and IoT has its impact on various areas like science, health, engineering, medicine, finance, business, and mainly, the society. Due to the growth in security intelligence, there is a requirement for new techniques which need big data and big data analytics. IoT security does not alone deal with the security of the device, but it also has to care about the web interfaces, cloud services, and other devices that interact with it. There are many techniques used for addressing challenges like privacy of individuals, inference, and aggregation, which makes it possible to re-identify individuals' even though they are removed from a dataset. It is understood that a few security vulnerabilities could lead to insecure web interface. This chapter discusses the challenges in security and how big data can be used for it. It also analyzes the various attacks and threat modeling in detail. Two case studies in two different areas are also discussed.

A Conceptual Model for the Organizational Adoption of Information System Security Innovations

Information system (IS) security threats are still a major concern for many organizations. However, most organizations fall short in achieving a successful adoption and implementation of IS security measures. In this chapter, the authors developed a theoretical model for the adoption process of IS security innovations in organizations. The model was derived by combining four theoretical models of innovation adoption, namely diffusion of innovation theory (DOI), the technology acceptance model (TAM), the theory of planned behavior (TPB), and the technology-organisation-environment (TOE) framework. The model depicts IS security innovation adoption in organizations, as two decision proceedings. The adoption process from the initiation stage until the acquisition of innovation is considered as a decision made by organisation while the process of innovation assimilation is assumed as a result of the user acceptance of innovation within the organization.

Botnet and Internet of Things (IoTs)

In today's internet world the internet of things (IoT) is becoming the most significant and developing technology. The primary goal behind the IoT is enabling more secure existence along with the improvement of risks at various life levels. With the arrival of IoT botnets, the perspective towards IoT products has transformed from enhanced living enabler into the internet of vulnerabilities for cybercriminals. Of all the several types of malware, botnet is considered as really a serious risk that often happens in cybercrimes and cyber-attacks. Botnet performs some predefined jobs and that too in some automated fashion. These attacks mostly occur in situations like phishing against any critical targets. Files sharing channel information are moved to DDoS attacks. IoT botnets have subjected two distinct problems, firstly, on the public internet. Most of the IoT devices are easily accessible. Secondly, in the architecture of most of the IoT units, security is usually a reconsideration. This particular chapter discusses IoT, botnet in IoT, and various botnet detection techniques available in IoT.

Bootstrapping Urban Planning

Rapid growth of embedded devices and population density in IoT-based smart cities provides great potential for business and opportunities in urban planning. For addressing the current and future needs of living, smart cities have to revitalize the potential of big data analytics. However, a colossal amount of sensitive information invites various computational challenges. Moreover, big data generated by the IoT paradigm acquires different characteristics as compared to traditional big data because it contains heterogeneous unstructured data. Despite various challenges in big data, enterprises are trying to utilize its true potential for providing proactive applications to the citizens. In this chapter, the author finds the possibilities of the role of big data in the efficient management of smart cities. Representative applications of big data, along with advantages and disadvantages, are also discussed. By delving into the ongoing research approaches in securing and providing privacy to big data, this chapter is concluded by highlighting the open research issues in the domain.

Leveraging Fog Computing and Deep Learning for Building a Secure Individual Health-Based Decision Support System to Evade Air Pollution

Globalization has led to critical influence of air pollution on individual health status. Insights to the menace of air pollution on individual's health can be achieved through a decision support system, built based on air pollution status and individual's health status. The wearable internet of things (wIoT) devices along with the air pollution monitoring sensors can gather a wide range of data to understand the effect of air pollution on individual's health. The high-level feature extraction capability of deep learning can extract productive patterns from these data to predict the future air quality index (AQI) values along with their amount of risks in every individual. The chapter aims to develop a secure decision support system that analyzes the events adversity by calculating the temporal health index (THI) of the individual and the effective air quality index (AQI) of the location. The proposed architecture utilizes fog paradigm to offload security functions by adopting deep learning algorithms to detect the malicious network traffic patterns from the benign ones.