K-Anonymity

A privacy model that ensures individuals cannot be uniquely identified in a dataset.

Understanding:

K-Anonymity is a privacy-preserving technique used to prevent data deanonymization by ensuring that each anonymized record is indistinguishable from at least k-1 other records.

Common Applications and Use Cases:

Healthcare Data Privacy – Protects patient records while allowing research.
Anonymizing Location Data – Ensures users cannot be uniquely identified.
Big Data & AI Model Training – Allows use of anonymized datasets without privacy risks.

Best Practices and Security Considerations:

Use Differential Privacy with K-Anonymity – Enhances privacy protection.
Ensure High ‘k’ Values for Stronger Anonymity – Small k-values can lead to re-identification.
Monitor for Re-Identification Attacks – Prevents data linkage vulnerabilities.