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Anonymization

Framework & ModelData, Privacy & MeasurementGeneral Sensitivity

Techniques that remove or obscure identifying details so individuals can’t be easily linked to sensitive data.

What This Really Means

Commonly described as deciding what information is shared and how it’s protected.

It tends to shift with sensitivity of the topic, audience, and consent.

Examples

Removing names and contact details from notes

Summarizing responses so individuals can’t be singled out

Summarizing responses so individuals can’t be singled out

Common Misunderstandings

Tap each myth to reveal the reality

Reality

Anonymization does not mean impossible to identify, and it refers to techniques that remove or obscure identifying details so individuals can’t be easily linked to sensitive data.

Reality

Privacy doesn’t matter if data can feel like ‘helpful’ sometimes, but Anonymization refers to techniques that remove or obscure identifying details so individuals can’t be easily linked to sensitive data.

Tags

#privacy#data-privacy-measurement#framework-model

Inside LoveIQ

We identify patterns related to Anonymization 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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