This open access book describes the technologies needed to construct a secure big data infrastructure that connects data owners, analytical institutions, and user institutions in a circle of trust. It begins by discussing the most relevant technical issues involved in creating safe and privacy-preserving big data distribution platforms, and especially focuses on cryptographic primitives and privacy-preserving techniques, which are essential prerequisites. The book also covers elliptic curve cryptosystems, which offer compact public key cryptosystems; and LWE-based cryptosystems, which are a type of post-quantum cryptosystem. Since big data distribution platforms require appropriate data handling, the book also describes a privacy-preserving data integration protocol and privacy-preserving classification protocol for secure computation. Furthermore, it introduces an anonymization technique and privacy risk evaluation technique. This book also describes the latest related findings in both the living safety and medical fields. In the living safety field, to prevent injuries occurring in everyday life, it is necessary to analyze injury data, find problems, and implement suitable measures. But most cases don’t include enough information for injury prevention because the necessary data is spread across multiple organizations, and data integration is difficult from a security standpoint. This book introduces a system for solving this problem by applying a method for integrating distributed data securely and introduces applications concerning childhood injury at home and school injury. In the medical field, privacy protection and patient consent management are crucial for all research. The book describes a medical test bed for the secure collection and analysis of electronic medical records distributed among various medical institutions. The system promotes big-data analysis of medical data with a cloud infrastructure and includes various security measures developed in our project to avoid privacy violations.

This open access book describes the technologies needed to construct a secure big data infrastructure that connects data owners, analytical institutions, and user institutions in a circle of trust. It begins by discussing the most relevant technical issues involved in creating safe and privacy-preserving big data distribution platforms, and especially focuses on cryptographic primitives and privacy-preserving techniques, which are essential prerequisites. The book also covers elliptic curve cryptosystems, which offer compact public key cryptosystems; and LWE-based cryptosystems, which are a type of post-quantum cryptosystem. Since big data distribution platforms require appropriate data handling, the book also describes a privacy-preserving data integration protocol and privacy-preserving classification protocol for secure computation. Furthermore, it introduces an anonymization technique and privacy risk evaluation technique. This book also describes the latest related findings in both the living safety and medical fields. In the living safety field, to prevent injuries occurring in everyday life, it is necessary to analyze injury data, find problems, and implement suitable measures. But most cases don't include enough information for injury prevention because the necessary data is spread across multiple organizations, and data integration is difficult from a security standpoint. This book introduces a system for solving this problem by applying a method for integrating distributed data securely and introduces applications concerning childhood injury at home and school injury. In the medical field, privacy protection and patient consent management are crucial for all research. The book describes a medical test bed for the secure collection and analysis of electronic medical records distributed among various medical institutions. The system promotes big-data analysis of medical data with a cloud infrastructure and includes various security measures developed in our project to avoid privacy violations. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

Cybersecurity has been gaining serious attention and recently has become an important topic of concern for organizations, government institutions, and largely for people interacting with digital online systems. As many individual and organizational activities continue to grow and are conducted in the digital environment, new vulnerabilities have arisen which have led to cybersecurity threats. The nature, source, reasons, and sophistication for cyberattacks are not clearly known or understood, and many times invisible cyber attackers are never traced or can never be found. Cyberattacks can only be known once the attack and the destruction have already taken place long after the attackers have left. Cybersecurity for computer systems has increasingly become important because the government, military, corporate, financial, critical infrastructure, and medical organizations rely heavily on digital network systems, which process and store large volumes of data on computer devices that are exchanged on the internet, and they are vulnerable to “continuous” cyberattacks. As cybersecurity has become a global concern, it needs to be clearly understood, and innovative solutions are required. The Handbook of Research on Advancing Cybersecurity for Digital Transformation looks deeper into issues, problems, and innovative solutions and strategies that are linked to cybersecurity. This book will provide important knowledge that can impact the improvement of cybersecurity, which can add value in terms of innovation to solving cybersecurity threats. The chapters cover cybersecurity challenges, technologies, and solutions in the context of different industries and different types of threats. This book is ideal for cybersecurity researchers, professionals, scientists, scholars, and managers, as well as practitioners, stakeholders, researchers, academicians, and students interested in the latest advancements in cybersecurity for digital transformation.

This book constitutes the refereed conference proceedings of the 20th Australasian Conference on Information Security and Privacy, ACISP 2015, held in Brisbane, QLD, Australia, in June/July 2015. The 28 revised full papers presented in this volume were carefully revised and selected from 112 submissions. The papers are organized in topical sections on symmetric cryptanalysis; public key cryptography; identity-based encryption; digital signatures; security protocols; privacy protocols; symmetric constructions; homomorphic encryption and obfuscation.

With the proliferation of devices connected to the internet and connected to each other, the volume of data collected, stored, and processed is increasing every day, which brings new challenges in terms of information security. As big data expands with the help of public clouds, traditional security solutions tailored to private computing infrastructures and confined to a well-defined security perimeter, such as firewalls and demilitarized zones (DMZs), are no longer effective. New security functions are required to work over the heterogenous composition of diverse hardware, operating systems, and network domains. Security, Privacy, and Forensics Issues in Big Data is an essential research book that examines recent advancements in big data and the impact that these advancements have on information security and privacy measures needed for these networks. Highlighting a range of topics including cryptography, data analytics, and threat detection, this is an excellent reference source for students, software developers and engineers, security analysts, IT consultants, academicians, researchers, and professionals.

Building Big Data Applications helps data managers and their organizations make the most of unstructured data with an existing data warehouse. It provides readers with what they need to know to make sense of how Big Data fits into the world of Data Warehousing. Readers will learn about infrastructure options and integration and come away with a solid understanding on how to leverage various architectures for integration. The book includes a wide range of use cases that will help data managers visualize reference architectures in the context of specific industries (healthcare, big oil, transportation, software, etc.). - Explores various ways to leverage Big Data by effectively integrating it into the data warehouse - Includes real-world case studies which clearly demonstrate Big Data technologies - Provides insights on how to optimize current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements