Linking de-identified datasets can unlock critical value for research and AI — but without the right safeguards, it also raises serious privacy risks. Even with private tokenization, data linkage can increase the chance of re-identification in ways many teams underestimate.
This session dives into common pitfalls in tokenization and cryptographic key management, as well as practical guardrails that help health systems preserve privacy while enabling the safe use of data use across research and product development lifecycles.
You'll walk away with a clear framework to evaluate your data-sharing workflows and control re-identification risk — with real-world scenarios from healthcare environments.
Insights include:
- How tokenization enables safe linkage across datasets without exposing identifiers, enabling analytics and AI
- What HIPAA doesn't cover — and why de-identification isn't a blanket safeguard
- How to structure cryptographic key management for secure, scalable data use
