Data Privacy, Video Edition - Nishant Bhajaria is a tutorial demonstrating how to build privacy into systems well. This useful book contains real-life approaches to put into practice data governance, legal compliance, and security audits. From the classification of data based on the level of privacy risk to creating technical solutions for organizing and finding data, you will learn how to share data with technical privacy features and organize for the deletion of data.
The founder, Nishant Bhajaria, who has worked at Google, Netflix, and Uber managing their privacy, provides simple and easy-to-understand definitions of the terms and laws concerning privacy. By the end of this course, Data Privacy, Video Edition - Nishant Bhajaria, you will be able to create, implement, and evaluate the outcomes of the privacy programs to enhance the users’ privacy without a significant increase in time and resources. Lack of clear policies and inadequate communication regarding data breaches, weak policies, and poor communication reduce a user’s confidence in your applications and could result in massive legal repercussions. Fortunately, Data Privacy, Video Edition - Nishant Bhajaria provides best practices and instructions on how to protect your data and satisfy your users simultaneously. So, there is no bureaucracy with workable solutions and the clever reuse of existing security tools that allow for establishing personal privacy goals and their accomplishment.
Data Privacy, Video Edition - Nishant Bhajaria 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
Who is this course for?
- This is for engineers and business leaders who wish to improve privacy standards.
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