Building LLMs with sensitive data: A practical guide to privacy and security
Know your data: what “sensitive” means in practice Why this matters for LLMs: leakage is real Modern models can memorize and later regurgitate rare or sensitive strings from training corpora. Research has demonstrated the extraction of training data from production LLMs via carefully crafted prompts, and a growing body of work on membership-inference risks. The […]
Ensuring data privacy and security in data annotation
Protect sensitive data during the annotation process. Discover best practices for ensuring data privacy while maintaining data quality.