Anonymization in the LLM Era: No Room for Compromise

Session Track: AI Engineering

Session Time: 01:30 pm - 02:00 pm November 6

Session description

In a world increasingly driven by Large Language Models, organizations face a difficult question: how can we leverage these powerful tools without compromising sensitive data? For some industries, finance, healthcare, government, security and privacy are non-negotiable. This talk will walk through real-world challenges we’ve encountered when dealing with anonymization in the LLM era. We'll look at the limits of traditional techniques, explore emerging strategies, and discuss the trade-offs between privacy, utility, and cost. This talk is less graph-heavy than usual, but highly relevant for the Neo4j community. After all, we’re the people building knowledge graphs, handling sensitive relationships, and connecting the dots in complex datasets. Privacy isn’t someone else’s problem, it’s ours too!