This research explores how Large Language Models can enrich anonymized data while preserving privacy protections.

• Investigates LLMs' capabilities to reconstruct useful information from anonymized datasets
• Identifies both opportunities and limitations of using AI for data enrichment
• Evaluates privacy risks and the effectiveness of anonymization techniques like k-anonymity
• Offers practical insights for organizations balancing data utility with privacy requirements

This work is particularly valuable for security professionals seeking to implement robust data privacy solutions while maintaining data utility for analysis and decision-making.

Augmenting Anonymized Data with AI: Exploring the Feasibility and Limitations of Large Language Models in Data Enrichment