Gartner Research

Selecting Data Masking Methods and Architecture

Published: 05 July 2011

ID: G00213064

Analyst(s): Ramon Krikken

Summary

Proliferation of data makes controlling confidentiality ever more important. Although encryption is generally used as a main control, it does not work in all circumstances. Data masking provides an alternative control that allows secured data to remain usable, but a careful understanding of masking techniques and architecture are required in order to ensure secrecy and privacy are appropriately protected for a given use case.

Table Of Contents

Summary of Findings

Analysis

  • Defining Data Masking
  • Masking Use Cases and Requirements
    • Data Masking and Data Management
    • Data Use Cases and Environments
    • Information Secrecy and Privacy Protection
    • Risk and Regulatory Requirements
    • Data Utility Requirements
  • Masking Architecture Choices
    • Static Data Masking
    • Dynamic Data Masking
    • Hybrid Data Masking
    • Masking Architecture and Security Objectives
  • Masking Technique Choices
    • Suppression
    • Redaction
    • Substitution
    • Generalization
    • Shuffling
    • Randomization

Comparing Architecture and Technique Choices

  • Make Masking Part of the Information Protection Program
  • Gain a Deep Understanding of Applications and Data
  • Implement Programs to Govern and Manage Data and Metadata
  • Choose the Appropriate Architecture Options
  • Choose the Right Techniques and Involve the Experts
  • Examine the Use of Suppression to Limit Exposure
  • Tread Carefully When Masking for Privacy
  • Understand the Limitations of Data Masking

The Details

  • The Science of Unmasking Data
    • Attacks on Secrecy
    • Attacks on Privacy

Conclusion

Recommended Reading

Notes

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