Video description
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
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-off s 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.
Quotes
I wish I had had this text in 2015 or 2016 at Netflix, and it would have been very helpful in 2008–2012 in a time of significant architectural evolution of our technology.
- From the Foreword by Neil Hunt, Former CPO, Netflix
Your guide to building privacy into the fabric of your organization.
- John Tyler, JPMorgan Chase
The most comprehensive resource you can find about privacy.
- Diego Casella, InvestSuite
Offers some valuable insights and direction for enterprises looking to improve the privacy of their data.
- Peter White, Charles Sturt University
Publisher resources
Table of contents
- Part 1. Privacy, data, and your business
- Chapter 1. Privacy engineering: Why it’s needed, how to scale it
- Chapter 1. How data flows into and within your company
- Chapter 1. Why privacy matters
- Chapter 1. Privacy: A mental model
- Chapter 1. How privacy affects your business at a macro level
- Chapter 1. Privacy tech and tooling: Your options and your choices
- Chapter 1. What this book will not do
- Chapter 1. How the role of engineers has changed, and how that has affected privacy
- Chapter 1. Summary
- Chapter 2. Understanding data and privacy
- Chapter 2. This could be your company
- Chapter 2. Data, your business growth strategy, and privacy
- Chapter 2. Examples: When privacy is violated
- Chapter 2. Privacy and the regulatory landscape
- Chapter 2. Privacy and the user
- Chapter 2. After building the tools comes the hard part: Building a program
- Chapter 2. As you build a program, build a privacy-first culture
- Chapter 2. Summary
- Part 2. A proactive privacy program: Data governance
- Chapter 3. Data classification
- Chapter 3. Why data classification is necessary
- Chapter 3. How you can implement data classification to improve privacy
- Chapter 3. How to classify data with a focus on privacy laws
- Chapter 3. The data classification process
- Chapter 3. Data classification: An example
- Chapter 3. Summary
- Chapter 4. Data inventory
- Chapter 4. Machine-readable tags
- Chapter 4. Creating a baseline
- Chapter 4. The technical architecture
- Chapter 4. Understanding the data
- Chapter 4. When should you start the data inventory process?
- Chapter 4. A data inventory is not a binary process
- Chapter 4. What does a successful data inventory process look like?
- Chapter 4. Summary
- Chapter 5. Data sharing
- Chapter 5. How to share data safely: Security as an ally of privacy
- Chapter 5. Obfuscation techniques for privacy-safe data sharing
- Chapter 5. Sharing internal IDs with third parties
- Chapter 5. Measuring privacy impact
- Chapter 5. Privacy harms: This is not a drill
- Chapter 5. Summary
- Part 3. Building tools and processes
- Chapter 6. The technical privacy review
- Chapter 6. Implementing the legal privacy review process
- Chapter 6. Making the case for a technical privacy review
- Chapter 6. Integrating technical privacy reviews into the innovation pipeline
- Chapter 6. Scaling the technical privacy review process
- Chapter 6. Sample technical privacy reviews
- Chapter 6. Summary
- Chapter 7. Data deletion
- Chapter 7. What does a modern data collection architecture look like?
- Chapter 7. How the data collection architecture works
- Chapter 7. Deleting account-level data: A starting point
- Chapter 7. Deleting account-level data: Automation and scaling for distributed services
- Chapter 7. Sensitive data deletion
- Chapter 7. Who should own data deletion?
- Chapter 7. Summary
- Chapter 8. Exporting user data: Data Subject Access Requests
- Chapter 8. Setting up the DSAR process
- Chapter 8. DSAR automation, data structures, and data flows
- Chapter 8. Internal-facing screens and dashboards
- Chapter 8. Summary
- Part 4. Security, scaling, and staffing
- Chapter 9. Building a consent management platform
- Chapter 9. A consent management platform
- Chapter 9. A data schema model for consent management
- Chapter 9. Consent code: Objects
- Chapter 9. Other useful capabilities in a CMP
- Chapter 9. Integrating consent management into product workflow
- Chapter 9. Summary
- Chapter 10. Closing security vulnerabilities
- Chapter 10. Protecting privacy by managing perimeter access
- Chapter 10. Protecting privacy by closing access-control gaps
- Chapter 10. Summary
- Chapter 11. Scaling, hiring, and considering regulations
- Chapter 11. The privacy engineering domain and skills
- Chapter 11. Privacy and the regulatory climate
- Chapter 11. Summary
Product information
- Title: Data Privacy, Video Edition
- Author(s):
- Release date: February 2022
- Publisher(s): Manning Publications
- ISBN: None
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