Privacy Attacks: Re-identification, Linkage, and k-Anonymity

Stripping direct identifiers does not anonymize data: quasi-identifiers like ZIP, birth date, and gender are close to unique for most people, and any auxiliary dataset sharing those fields enables a linkage attack. k-anonymity, l-diversity, and t-closeness each patch the previous one's hole and all of them collapse in high dimensions, where nearly every record is unique. AI, ML, and GenAI interviews probe this because it is the reason differential privacy exists, and because sparse behavioral data, embeddings, and aggregate model outputs all leak in ways a redaction pass cannot fix.