Data Privacy: A runbook for engineers

by Nishant Bhajaria

Overview

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

Product Details

ISBN-13:	9781638357186
Publisher:	Manning
Publication date:	03/22/2022
Sold by:	SIMON & SCHUSTER
Format:	eBook
Pages:	384
File size:	10 MB

About the Author

Nishant Bhajaria leads the Technical Privacy and Strategy teams for Uber. He heads a large team that includes data scientists, engineers, privacy experts and others as they seek to improve data privacy for the customers and the company. His role has significant levels of cross-functional visibility and impact. Previously he worked in compliance, data protection, security, and privacy at Google. He was also the head of privacy engineering at Netflix. He is a well-known expert in the field of data privacy, has developed numerous courses on the topic, and has spoken extensively at conferences and podcasts.

Table of Contents

Foreword xiii

Preface xvii

Acknowledgments xix

About this book xxi

About the author xxiv

About the cover illustration xxvi

Part 1 Privacy, Data, and Your Business 1

1 Privacy engineering: Why it's needed, how to scale it 3

1.1 What is privacy? 4

1.2 How data flows into and within your company 7

1.3 Why privacy matters 9

The fines are real 9

Early-stage efficiency wins can cause late-stage privacy headaches 11

Privacy investigations could be more than a speed bump 13

Privacy process can unlock business opportunities: A real-life example 18

1.4 Privacy: A mental model 20

1.5 How privacy affects your business at a macro level 22

Privacy and safety: The COVID edition 23

Privacy and regulations: A cyclical process 24

1.6 Privacy tech and tooling: Your options and your choices 26

The "build vs. buy" question 27

Third-party privacy tools: Do they really work and scale? 28

The risks in buying third-party privacy tools 31

1.7 What this book will not do 32

1.8 How the role of engineers has changed, and how that has affected privacy 32

2 Understanding data and privacy 35

2.1 Privacy and what it entails 36

Why privacy is hard 36

Privacy engineering on the ground: What you have to accomplish 37

Privacy, data systems, and policy enforcement 39

2.2 This could be your company 41

2.3 Data, your business growth strategy, and privacy 45

2.4 Examples: When privacy is violated 46

Equifax 47

The Office of Personnel Management (OPM) breach 48

LabCorp and Quest Diagnostics 50

2.5 Privacy and the regulatory landscape 51

How regulations impact your product and their users 51

How your program should help prepare for changing privacy law 53

2.6 Privacy and the user 53

Becoming an American, and privacy 53

Today's users and their privacy concerns 54

2.7 After building the tools comes the hard part: Building a program 55

2.8 As you build a program, build a privacy-first culture 58

Part 2 A proactive privacy program: Data governance 61

3 Data classification 63

3.1 Data classification and customer context 64

3.2 Why data classification is necessary 65

Data classification as part of data governance 66

Data classification How it helps align priorities 67

Industry benchmarking around data classification 73

Unstructured data and governance 74

Data classification as part of your maturity journey 75

3.3 How YOU can implement data classification to improve privacy 78

Data classification and access options 78

Data classification, access management, and privacy: Example 1 79

Data classification, access management, and privacy: Example, 2 81

3.4 How to classify data with a focus on privacy laws 82

Data classification as an abstraction of privacy laws 82

Data classification to resolve tension between interpretations of privacy laws 83

3.5 The data classification process 84

Working with cross-functional stakeholders on your data classification 85

Formalizing and refactoring your data classification 87

The data classification process: A Microsoft template 88

3.6 Data classification: An example 90

4 Data inventory 94

4.1 Data inventory: What it is and why you need it 95

4.2 Machine-readable tags 97

What are data inventory tags? 97

Data inventory tags: A specific example 98

4.3 Creating a baseline 102

4.4 The technical architecture 103

Structured and unstructured data 103

Data inventory architectural capabilities 106

Data inventory workflow 108

4.5 Understanding the data 111

The metadata definition process 111

The metadata discovery process 113

4.6 When should you start the data inventory process? 114

Why is the data inventory process so hard? 114

Data inventory: Sooner is better than later 115

4.7 A data inventory is not a binary process 117

Data inventory level 1 117

Data inventory level 2 119

Data inventory level 3 120

4.8 What does a successful data inventory process look like? 122

Data inventory objective success metrics 122

Data inventory subjective success metrics 123

5 Data sharing 125

5.1 Data sharing: Why companies need to share data 126

Data sharing: Taxicab companies 127

Data sharing: Online advertising 128

Privacy in advertising 132

5.2 How to share data safely: Security as an ally of privacy 134

Tracking President Trump 134

Protecting data in motion 135

Protecting data at rest 137

5.3 Obfuscation techniques for privacy-safe data sharing 140

Data sharing and US national security 140

Data anonymization: The relationship between precision and retention 142

Data anonymization: The relationship between precision and access 143

Data anonymization: Mapping universal IDs to internal IDs 146

5.4 Sharing internal IDs with third parties 148

Use case 1: Minimal session (no linking of user activity is needed) 149

Use case 2: Single session per dataset (linking of the same user's activity within a dataset) 149

Use case 3: Session spanning datasets (linking across datasets) 150

Recovering pseudonymized values 150

5.5 Measuring privacy impact 151

K-anonymity 152

L-diversity 155

5.6 Privacy harms: This is not a drill 156

Facebook and Cambridge Analytica 156

Sharing data and weaknesses 158

Part 3 Building Tools and Processes 159

6 The technical privacy review 161

6.1 What are privacy reviews? 162

The privacy impact assessment (PIA) 164

The data protection impact assessment (DPIA) 165

6.2 Implementing the legal privacy review process 170

6.3 Making the case for a technical privacy review 172

Timing and scope 172

What the technical review covers that the legal review does not 174

6.4 Integrating technical privacy reviews into the innovation pipeline 177

Where does the technical privacy review belong? 177

How to implement a technical privacy intake? 178

6.5 Scaling the technical privacy review process 184

Data sharing 184

Machine-learning models 185

6.6 Sample technical privacy reviews 187

Messaging apps and engagement apps: Do they connect? 187

Masks and contact tracing 189

7 Data deletion 192

7.1 Why must a company delete data? 193

7.2 What does a modern data collection architecture look like? 194

Distributed architecture and microservices: How companies collect data 195

How real-time data is stored and accessed 196

Archival data storage 197

Other data storage locations 198

How data storage grows from collection to archival 199

7.3 How the data collection architecture works 201

7.4 Deleting account-level data: A starting point 202

Account deletion: Building the tooling and- process 203

Scaling account deletion 203

7.5 Deleting account-level data: Automation and scaling for distributed services 205

Registering services and data fields for deletion 207

Scheduling data deletion 209

7.6 Sensitive data deletion 210

7.7 Who should own data deletion? 213

8 Exporting user data: Data Subject Access Requests 216

8.1 What are DSARs? 217

What rights do DSAR regulations give to users? 220

An overview of the DSAR request fulfillment process 221

8.2 Setting up the DSAR process 224

The key steps in creating a DSAR system 224

Building a DSAR status dashboard 226

8.3 DSAR automation, data structures, and data flows 228

DSAR components 228

Cuboids: A subset of DSAR data 230

DSAR templates 232

Data sources for DSAR templates 234

8.4 Internal-facing screens and dashboards 236

Part 4 Security, Scaling, and Staffing 245

9 Building a consent management platform 247

9.1 Why consent management is important 248

Consent management and privacy-related regulation 249

Consent management and tech industry changes 251

Consent management and your business 252

9.2 A consent management platform 253

9.3 A data schema model for consent management 256

The entity relationships that help structure a CMP 256

Entity relationship schemas: A CMP database 257

9.4 Consent code: Objects 263

API to check consent status 264

API to retrieve disclosures 266

API to update the consent status for a disclosure 268

API to process multiple disclosures 271

API to register with the consents service 274

Useful definitions for the consents service 275

9.5 Other useful capabilities in a CMP 276

9.6 Integrating consent management into product workflow 278

10 Closing security vulnerabilities 282

10.1 Protecting privacy by reducing the attack surface 284

Managing the attack surface 284

How testing can cause security and privacy risks 285

An enterprise risk model for security and privacy 289

10.2 Protecting privacy by managing perimeter access 295

The Target breach 295

MongoDB security weaknesses 302

Authorization best practices 305

Why continuous monitoring of accounts and credentials is important 313

Remote work and privacy risk 314

10.3 Protecting privacy by closing access-control gaps 316

How an IDOR vulnerability works 316

IDOR testing and mitigation 319

11 Scaling, hiring, and considering regulations 322

11.1 A maturity model for privacy engineering 324

Identification 326

Protection 329

Detection 336

Remediation 338

11.2 The privacy engineering domain and skills 339

11.3 Privacy and the regulatory climate 342

Index 347