Quasi-Identifiers
Data fields that don’t identify someone alone but can identify them when combined (e.g., age, city, job title, rare attributes).
What This Really Means
Quasi-identifiers are a major driver of re-identification, especially in small populations, small countries/regions, or niche communities.
Good governance uses aggregation, coarsening, suppression, and access tiering to reduce risk while preserving research value.
Examples
“Age 47, small town, niche profession” uniquely identifies someone
Combining region + uncommon preference flags a person
Removing exact dates and using age ranges reduces risk.
Common Misunderstandings
Tap each myth to reveal the reality
Quasi-Identifiers points to data fields that don’t identify someone alone but can identify them when combined (e.g., age, city, job title, rare attributes), so quasi-identifiers are harmless because they aren’t names is a misunderstanding.
If data is aggregated, risk isn’t always always gone, and Quasi-Identifiers is about data fields that don’t identify someone alone but can identify them when combined (e.g., age, city, job title, rare attributes).
Quasi-Identifiers points to data fields that don’t identify someone alone but can identify them when combined (e.g., age, city, job title, rare attributes), so only celebrities can be re-identified is a misunderstanding.
Quasi-identifiers don’t matter if the dataset can feel like “internal.” sometimes, but Quasi-Identifiers refers to data fields that don’t identify someone alone but can identify them when combined (e.g., age, city, job title, rare attributes).
Related Terms
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Inside LoveIQ
We identify patterns related to Quasi-Identifiers by analyzing responses in our assessment modules, helping you understand your unique relationship dynamics.
Sample visualization of a gap metric.
“You don't need to label yourself. These terms help describe patterns — not define you.”
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