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Context

As explained in Effective Data Analysis

Contextual information is a combination of non-sensitive details that provides enough clues to infer an individual's identity.

Contextual information combines non-sensitive details that can provide enough clues to identify an individual. Removing names, email addresses, and other personally identifiable information does not necessarily prevent deanonymization. section 8.3.3

Identification becomes possible when records are cross-referenced with public information. Dates, ratings, review text, travel patterns, demographics, search queries, or small-group survey results can make people recognizable. section 8.3.3

Common reidentification paths include:

  • Matching movie ratings and review details with publicly available IMDb ratings. section 8.3.3
  • Linking recurring transit-card travel patterns with social-media check-ins. section 8.3.3
  • Combining age, gender, race, ethnicity, and ZIP code to narrow identities. section 8.3.3
  • Inspecting search queries or small survey groups whose details reveal specific people. section 8.3.3
Figure 8.13 An employee engagement survey with multiple breakouts can accidentally deanonymize individuals in groups with few members. For example, the sales department has one EEOC group with n=1, making it easy to identify the person and their satisfaction.
figure

Use the figure to see how multiple survey breakouts can expose an individual in a group with only one member. section 8.3.3

How Effective Data Analysis covers it

How deep it goes
Defines contextual information and illustrates cross-referencing risks, but stops short of specifying a formal risk-assessment procedure.
Examples
Uses Netflix ratings, IMDb, transit cards, social-media check-ins, demographics, AOL searches, and small employee-survey groups.
What it assumes
Assumes familiarity with direct identifiers and anonymization while teaching that non-sensitive combinations can still identify people.
What you can do after
Evaluate whether removing direct identifiers adequately protects people by examining combinations that could be matched with public information.
How it connects
Connects contextual information to personally identifiable information, anonymization, deanonymization, public data, and privacy.

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This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.