Data Privacy And Security
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Data Privacy and Security
Author: David Salomon
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Covering classical cryptography, modern cryptography, and steganography, this volume details how data can be kept secure and private. Each topic is presented and explained by describing various methods, techniques, and algorithms. Moreover, there are numerous helpful examples to reinforce the reader's understanding and expertise with these techniques and methodologies. Features & Benefits: * Incorporates both data encryption and data hiding * Supplies a wealth of exercises and solutions to help readers readily understand the material * Presents information in an accessible, nonmathematical style * Concentrates on specific methodologies that readers can choose from and pursue, for their data-security needs and goals * Describes new topics, such as the advanced encryption standard (Rijndael), quantum cryptography, and elliptic-curve cryptography. The book, with its accessible style, is an essential companion for all security practitioners and professionals who need to understand and effectively use both information hiding and encryption to protect digital data and communications. It is also suitable for self-study in the areas of programming, software engineering, and security.
Privacy and Security Issues in Big Data
This book focuses on privacy and security concerns in big data and differentiates between privacy and security and privacy requirements in big data. It focuses on the results obtained after applying a systematic mapping study and implementation of security in the big data for utilizing in business under the establishment of “Business Intelligence”. The chapters start with the definition of big data, discussions why security is used in business infrastructure and how the security can be improved. In this book, some of the data security and data protection techniques are focused and it presents the challenges and suggestions to meet the requirements of computing, communication and storage capabilities for data mining and analytics applications with large aggregate data in business.
Data Privacy
Author: Nishant Bhajaria
language: en
Publisher: Simon and Schuster
Release Date: 2022-03-22
Engineer privacy into your systems with these hands-on techniques for data governance, legal compliance, and surviving security audits. In Data Privacy you will learn how to: Classify data based on privacy risk Build technical tools to catalog and discover data in your systems Share data with technical privacy controls to measure reidentification risk Implement technical privacy architectures to delete data Set up technical capabilities for data export to meet legal requirements like Data Subject Asset Requests (DSAR) Establish a technical privacy review process to help accelerate the legal Privacy Impact Assessment (PIA) Design a Consent Management Platform (CMP) to capture user consent Implement security tooling to help optimize privacy Build a holistic program that will get support and funding from the C-Level and board Data Privacy teaches you to design, develop, and measure the effectiveness of privacy programs. You’ll learn from author Nishant Bhajaria, an industry-renowned expert who has overseen privacy at Google, Netflix, and Uber. The terminology and legal requirements of privacy are all explained in clear, jargon-free language. The book’s constant awareness of business requirements will help you balance trade-offs, and ensure your user’s privacy can be improved without spiraling time and resource costs. About the technology Data privacy is essential for any business. Data breaches, vague policies, and poor communication all erode a user’s trust in your applications. You may also face substantial legal consequences for failing to protect user data. Fortunately, there are clear practices and guidelines to keep your data secure and your users happy. About the book Data Privacy: A runbook for engineers teaches you how to navigate the trade-offs between strict data security and real world business needs. In this practical book, you’ll learn how to design and implement privacy programs that are easy to scale and automate. There’s no bureaucratic process—just workable solutions and smart repurposing of existing security tools to help set and achieve your privacy goals. What's inside Classify data based on privacy risk Set up capabilities for data export that meet legal requirements Establish a review process to accelerate privacy impact assessment Design a consent management platform to capture user consent About the reader For engineers and business leaders looking to deliver better privacy. About the author Nishant Bhajaria leads the Technical Privacy and Strategy teams for Uber. His previous roles include head of privacy engineering at Netflix, and data security and privacy at Google. Table of Contents PART 1 PRIVACY, DATA, AND YOUR BUSINESS 1 Privacy engineering: Why it’s needed, how to scale it 2 Understanding data and privacy PART 2 A PROACTIVE PRIVACY PROGRAM: DATA GOVERNANCE 3 Data classification 4 Data inventory 5 Data sharing PART 3 BUILDING TOOLS AND PROCESSES 6 The technical privacy review 7 Data deletion 8 Exporting user data: Data Subject Access Requests PART 4 SECURITY, SCALING, AND STAFFING 9 Building a consent management platform 10 Closing security vulnerabilities 11 Scaling, hiring, and considering regulations