What strategy involves hiding data in a data set to prevent someone from identifying specific individuals based on other data fields present?

Question

Options

• AAnonymization
• BTokenization
• CMasking
• DObfuscation

• A
  91% (20)
• B
  5% (1)
• D
  5% (1)

Explanation

Anonymization is correct because it specifically refers to the process of removing or altering personally identifiable information (PII) from a dataset so that individuals cannot be re-identified - even when other data fields (like age, zip code, or job title) are still present. This is the textbook definition of protecting individual identity within a dataset while preserving the data's analytical value.

Tokenization (B) replaces sensitive data with a non-sensitive placeholder (token) that maps back to the original via a secure lookup - it's about substitution with reversibility, not identity prevention. Masking (C) obscures specific data values (e.g., showing ****1234 for a credit card) to hide the value itself, not to prevent re-identification from combined fields. Obfuscation (D) is a broader term meaning to make something unclear or confusing - it's not a defined data privacy technique and doesn't specifically address individual identification.

Memory tip: Think "Anon = Anonymous person" - anonymization makes people anonymous in a dataset, which is exactly what the question describes. If you see "prevent identifying individuals," think anonymization.

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