1st Edition

The Complete Book of Data Anonymization From Planning to Implementation

By Balaji Raghunathan Copyright 2013
    267 Pages 95 B/W Illustrations
    by Auerbach Publications

    The Complete Book of Data Anonymization: From Planning to Implementation supplies a 360-degree view of data privacy protection using data anonymization. It examines data anonymization from both a practitioner's and a program sponsor's perspective. Discussing analysis, planning, setup, and governance, it illustrates the entire process of adapting and implementing anonymization tools and programs.

    Part I of the book begins by explaining what data anonymization is. It describes how to scope a data anonymization program as well as the challenges involved when planning for this initiative at an enterprisewide level.

    Part II describes the different solution patterns and techniques available for data anonymization. It explains how to select a pattern and technique and provides a phased approach towards data anonymization for an application.

    A cutting-edge guide to data anonymization implementation, this book delves far beyond data anonymization techniques to supply you with the wide-ranging perspective required to ensure comprehensive protection against misuse of data.

    Overview of Data Anonymization
    Points to Ponder
    What is Data Anonymization?
    What are the Drivers for Data Anonymization?
         The Need To Protect Sensitive Data Handled As Part Of Business
         Increasing Instances of Insider Data Leakage, Misuse of Personal Data and the Lure of Money for Mischievous Insiders
         Employees Getting Even With Employers
         Negligence of Employees to Sensitivity of Personal Data
         Astronomical Cost to the Business due to Misuse of Personal Data
    Risks Arising out of Operational Factors Like Outsourcing and Partner Collaboration 
         Outsourcing Of IT Application Development, Testing And Support
         Increasing Collaboration With Partners
         Legal and Compliance Requirements
    Will Procuring and Implementing a Data Anonymization Tool by Itself Ensure Protection of Privacy of Sensitive Data?
         Ambiguity of Operational Aspects
         Allowing the Same Users to Access both Masked and Unmasked Environment
         Lack Of Buy-In From IT Application Developers, Testers and End-Users
         Compartmentalized Approach to Data Anonymization
         Absence of Data Privacy Protection Policies or Weak enforcement of Data Privacy Policies
    Benefits Of Data Anonymization Implementation


    Enterprise Data Privacy Governance Model
    Points to Ponder
    Chief Privacy Officer
    Unit /Department Privacy Compliance Officers
    The Steering Committee for Data Privacy Protection Initiatives
         Management Representatives
         Information Security And Risk Department Representatives
         Representatives from the Department Security and Privacy Compliance Officers
    Incident Response Team
    The Role of the Employee in Privacy Protection
    The Role of the CIO
    Typical Ways Enterprises Enforce Privacy Policies

    Enterprise Data Classification Policy and Privacy Laws
    Points to Ponder
    Regulatory Compliance
    Enterprise Data Classification
    Points to Consider
    Controls For Each Class Of Enterprise Data

    Operational Processes, Guidelines and Controls for Enterprise Data Privacy Protection
    Points to Ponder
    Privacy Incident Management
    Planning for Incident Resolution
         Incident Capture
         Incident Response
         Post Incident Analysis
    Guidelines and Best Practices
         PII/PHI Collection Guidelines
         Guidelines for Storage and Transmission of PII/PHI
         PII/PHI Usage Guidelines
         Guidelines for Storing PII/PHI on Portable Devices and Storage Devices 
         Guidelines for Staff

    The Different Phases of a Data Anonymization Program
    Points to Ponder
    How Should I Go about the Enterprise Data Anonymization Program?
         The Assessment Phase
         Tool Evaluation and Solution Definition Phase
         Data Anonymization Implementation Phase
         Operations Phase or the Steady-State phase
    Food For Thought
         When Should the Organization Invest on a Data Anonymization Exercise?
         The Organization’s Security Policies Anyway Mandate Authorization to be Built-in For Every Application. Won’t This be Sufficient? Why is Data Anonymization Needed?
         Is there a Business Case for Data Anonymization Program in My Organization?
         When Can a Data Anonymization Program be Called as a Successful One?
         Why Should I go for a Data Anonymization Tool when SQL Encryption Scripts Can be Used to Anonymize Data?
         What are the Benefits Provided by Data Masking Tools for Data Anonymization?
         Why is a Tool Evaluation Phase Needed?
         Who Should Implement Data Anonymization? Should it be the Tool Vendor or the IT Service Partner or External Consultants or Internal Employees?
         How Many Rounds of Testing Must be Planned to Certify that Application Behavior is Unchanged with use of Anonymized Data?

    Departments Involved in Enterprise Data Anonymization Program
    Points to Ponder
    The Role of the Information Security and Risk Department
    The Role of the Legal Department
    The Role of Application Owners and Business Analysts
    The Role of Administrators
    The Role of the Project Management Office (PMO)
    The Role of the Finance department
    Steering Committee

    Privacy Meter- Assessing The Maturity Of Data Privacy Protection Practices In The Organization
    Points to Ponder
    Planning A Data Anonymization Implementation
    Data Privacy Maturity Model

    Enterprise Data Anonymization Execution Model
    Points to Ponder
    Decentralized Model
    Centralized Anonymization Setup
    Shared Services Model

    Tools and Technology
    Points to Ponder
    Shortlisting Tools for Evaluation
    Tool Evaluation and Selection
         Functional Capabilities 
         Technical Capabilities
         Operational Capabilities
         Financial Parameters
    Scoring criteria for Evaluation

    Anonymization Implementation – Activities & Effort
    Points to Ponder
    Anonymization Implementation Activities For An Application
         Application Anonymization Analysis and Design
         Anonymization Environment Setup
         Application Anonymization Configuration and Build
         Anonymized Application Testing
    Complexity Criteria
         Application Characteristics
         Environment Dependencies
    Arriving at an Effort Estimation Model
         Definition of Complexity Criteria
         Ready-Reckoner Preparation
         Determination Of The Complexity Of The Application To Be Anonymized
         Assignment of Effort to Each Activity Based on the Ready-Reckoner
    Case Study
         Estimation Approach
         Application Complexity
         Arriving at a Ball Park Estimate

    The Next Wave of Data Privacy Challenges


    Data Anonymization Patterns
    Points to Ponder
    Pattern Overview

    Data State Anonymization Patterns
    Points to Ponder
    Principles of Anonymization
    Static Masking Patterns
         EAL Pattern (Extract Anonymize Load Pattern)
         ELA Pattern (Extract Load Anonymize Pattern)
         Data Subsetting
    Dynamic Masking
    Dynamic Masking Patterns
         Interception Pattern
         Invocation Patterns
         Application of Dynamic Masking patterns
    Dynamic Masking vs. Static Masking

    Anonymization Environment Patterns
    Points to Ponder
    Typical Application Environments in an enterprise
    Testing Environments
         Standalone Environment
         Integration Environment
         Automated Integration Test environment
         Scaled-Down Integration Test Environment

    Data Flow Patterns Across Environments
    Points to Ponder
    Flow of Data from Production Environment Databases to Non-Production Environment Databases
    Movement of Anonymized Files from Production Environment to Non-Production Environments
    Masked Environment for Integration Testing-Case Study

    Data Anonymization Techniques
    Points to Ponder
    Basic Anonymization Techniques
         Number Variance
         Date Variance
         Nulling Out
         Character Masking
         Cryptographic Techniques
    Partial Sensitivity and Partial Masking
    Masking Based on External Dependency
    Auxiliary Anonymization Techniques
    Alternate Classification of Data Anonymization Techniques
         Substitution Techniques
         Translation Techniques
    Leveraging Data Anonymization Techniques

    Data Anonymization Implementation
    Points to Ponder
    Pre-Requisites Before Starting The Anonymization Implementation Activities
         Sensitivity Definition Readiness - What is Considered as Sensitive Data by the Organization?
         Sensitive Data Discovery- Where does Sensitive Data Exist?
    Application Architecture Analysis
    Application Sensitivity Analysis
         What is Sensitivity Level and How Do We Prioritize Sensitive Fields for Treatment?
    Anonymization Design Phase
    Anonymization Implementation, Testing, and Rollout Phase
    Anonymization Operations
    Incorporation of Privacy protection procedures as part of Software Development Life Cycle and Application Lifecycle for New Applications
         Impact on SDLC Team
    Challenges Faced as part of Any Data Anonymization Implementation
    Best Practices To Ensure Success Of Anonymization Projects



    Balaji Raghunathan has more than 20 years of experience in the software industry. As part of his current role as General Manager, Technology Consulting & Enterprise Architecture, at ITC Infotech, Balaji Raghunathan is responsible for helping the clients of ITC Infotech simplify their technology landscape, assess their readiness for digital initiatives, modernize their technology architecture and prepare them for their digital journey

    Balaji Raghunathan has also lead the delivery of digital projects for banking, financial services, and insurance customers as well as helped them define their digital strategy. He has lead strategy engagements for enterprise mobility initiatives as well as developed, managed and commercialized intellectual property (IP) during his prior stints with Capgemini and Infosys. During the last decade, Balaji Raghunathan has been involved in architecting software solutions for the energy, utilities, publishing, transportation, retail, and banking industries

    Balaji Raghunathan’s core areas of interest revolves around digital technology strategy, data privacy management and enterprise mobility. He is an avid blogger on Digital Technology Strategy, and has authored the book "The Complete Book of Data Anonymization-From Planning to Implementation". He has also the co-authored a chapter "Mobility and Its Impact on Enterprise Security" for the book "Information Security Management Handbook, Sixth Edition, Volume 7."

    He holds a patent on "System and Method for Runtime Data Anonymization" and has a pending patent on "System and Method for categorization of Social Media Conversation for Response Management."

    He is a TOGAF 8.0 and ICMG-WWISA Certified Software Architect.

    Balaji Raghunathan has a postgraduate diploma in business administration (finance) from Symbiosis Institute (SCDL), Pune, India and has an engineering degree (electrical and electronics) from Bangalore University, India. He has also completed a Senior Leadership Certificate course from Indian Institute of Management, Kozhikode.

    With more and more regulations focusing on protection of data privacy and prevention of misuse of personal data, anonymization of sensitive data is becoming a critical need for corporate and governmental organizations. This book provides a comprehensive view of data anonymization both from a program sponsor’s perspective as well as a practitioner’s. The special focus on implementation of  data anonymization across the enterprise makes this a valuable reference book for large data anonymization implementation programs.
    Prasad Joshi, Vice President, Infosys Labs, Infosys Ltd.

    This book on data anonymization could not have come at a better time, given the rapid adoption of outsourcing within enterprises and an ever increasing growth of business data. This book is a must read for enterprise data architects and data managers grappling with the problem of balancing the needs of application outsourcing with the requirements for strong data privacy.
    Dr. Pramod Varma, Chief Architect, Unique Identification Authority of India