Anonymization Reversal

Anonymization reversal de-anonymizes data supposedly made safe through k-anonymity, l-diversity, t-closeness, pseudonymization, or generalization. The fundamental vulnerability is that anonymization assumes a static threat model — a fixed amount of auxiliary information available to the attacker. In practice, attackers accumulate auxiliary data over time from breaches, public records, social media, and other model outputs.

• Re-identification risk scoring: proactively test anonymized datasets against known auxiliary sources
• Query pattern analysis: detect systematic probing correlating anonymized data with quasi-identifiers
• Multi-attribute query monitoring for de-anonymization-typical combinations
• Behavioral anomaly: sequential queries systematically narrowing an anonymity set

De-anonymization transforms "safe" datasets into PII-rich sources feeding all T10 extraction and inference techniques. In the LLM context, de-anonymized training data enables T10-AT-001 (Training Data Extraction) to target specific individuals.