Building An Anonymization Pipeline

How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner. Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing device and IoT data, based on collection models and use cases that address real business needs. These examples come from some of the most demanding data environments, such as healthcare, using approaches that have withstood the test of time. Create anonymization solutions diverse enough to cover a spectrum of use cases Match your solutions to the data you use, the people you share it with, and your analysis goals Build anonymization pipelines around various data collection models to cover different business needs Generate an anonymized version of original data or use an analytics platform to generate anonymized outputs Examine the ethical issues around the use of anonymized data

How can you use data in a way that protects individual privacy, but still ensures that data analytics will be useful and meaningful? With this practical book, data architects and engineers will learn how to implement and deploy anonymization solutions within a data collection pipeline. You'll establish and integrate secure, repeatable anonymization processes into your data flows and analytics in a sustainable manner. Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing data, based on data collection models and use cases enabled by real business needs. These examples come from some of the most demanding data environments, using approaches that have stood the test of time.

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions Methods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure

Using Continuous Delivery, you can bring software into production more rapidly, with greater reliability. A Practical Guide to Continuous Delivery is a 100% practical guide to building Continuous Delivery pipelines that automate rollouts, improve reproducibility, and dramatically reduce risk. Eberhard Wolff introduces a proven Continuous Delivery technology stack, including Docker, Chef, Vagrant, Jenkins, Graphite, the ELK stack, JBehave, and Gatling. He guides you through applying these technologies throughout build, continuous integration, load testing, acceptance testing, and monitoring. Wolff’s start-to-finish example projects offer the basis for your own experimentation, pilot programs, and full-fledged deployments. A Practical Guide to Continuous Delivery is for everyone who wants to introduce Continuous Delivery, with or without DevOps. For managers, it introduces core processes, requirements, benefits, and technical consequences. Developers, administrators, and architects will gain essential skills for implementing and managing pipelines, and for integrating Continuous Delivery smoothly into software architectures and IT organizations. Understand the problems that Continuous Delivery solves, and how it solves them Establish an infrastructure for maximum software automation Leverage virtualization and Platform as a Service (PAAS) cloud solutions Implement build automation and continuous integration with Gradle, Maven, and Jenkins Perform static code reviews with SonarQube and repositories to store build artifacts Establish automated GUI and textual acceptance testing with behavior-driven design Ensure appropriate performance via capacity testing Check new features and problems with exploratory testing Minimize risk throughout automated production software rollouts Gather and analyze metrics and logs with Elasticsearch, Logstash, Kibana (ELK), and Graphite Manage the introduction of Continuous Delivery into your enterprise Architect software to facilitate Continuous Delivery of new capabilities

This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.

Protect your organization's security at all levels by introducing the latest strategies for securing DevOps Key Features Integrate security at each layer of the DevOps pipeline Discover security practices to protect your cloud services by detecting fraud and intrusion Explore solutions to infrastructure security using DevOps principles Book Description DevOps has provided speed and quality benefits with continuous development and deployment methods, but it does not guarantee the security of an entire organization. Hands-On Security in DevOps shows you how to adopt DevOps techniques to continuously improve your organization’s security at every level, rather than just focusing on protecting your infrastructure. This guide combines DevOps and security to help you to protect cloud services, and teaches you how to use techniques to integrate security directly in your product. You will learn how to implement security at every layer, such as for the web application, cloud infrastructure, communication, and the delivery pipeline layers. With the help of practical examples, you’ll explore the core security aspects, such as blocking attacks, fraud detection, cloud forensics, and incident response. In the concluding chapters, you will cover topics on extending DevOps security, such as risk assessment, threat modeling, and continuous security. By the end of this book, you will be well-versed in implementing security in all layers of your organization and be confident in monitoring and blocking attacks throughout your cloud services. What you will learn Understand DevSecOps culture and organization Learn security requirements, management, and metrics Secure your architecture design by looking at threat modeling, coding tools and practices Handle most common security issues and explore black and white-box testing tools and practices Work with security monitoring toolkits and online fraud detection rules Explore GDPR and PII handling case studies to understand the DevSecOps lifecycle Who this book is for Hands-On Security in DevOps is for system administrators, security consultants, and DevOps engineers who want to secure their entire organization. Basic understanding of Cloud computing, automation frameworks, and programming is necessary.

Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI).