Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models
arXiv:2606.03399v1 Announce Type: new Abstract: While large language models (LLMs) are increasingly used for clinical applications, many existing pipelines require sending raw sensitive health information to remote servers for processing, which heightens the risk of privacy leakage. A natural approach to mitigate this risk is to encrypt the data before transmission. However, straightforward solutions such as encrypting the entire dataset introduce prohibitive computational, alignment, and communication overheads, rendering large-scale practical deployment infeasible. To preserve privacy while
The increasing use of LLMs in sensitive domains like healthcare necessitates immediate solutions for data privacy to enable their broader adoption and regulatory compliance.
This development addresses a critical barrier to deploying powerful AI technologies in privacy-sensitive sectors, enabling new applications while mitigating significant risks.
The ability to selectively redact sensitive information cryptographically means that clinical LLM deployments can proceed with significantly reduced privacy leakage risks, lowering operational overheads compared to full encryption.
- · Healthcare AI Developers
- · Patients
- · Clinical Research Organizations
- · Cloud Providers
- · Legacy Data Security Firms
- · Ad-hoc Privacy Solutions
Wider and faster adoption of LLMs in healthcare due to improved data security.
Development of new AI-powered clinical applications that rely on sensitive patient data with greater confidence.
Potential for new regulatory frameworks that mandate similar selective redaction techniques for AI deployments in other sensitive industries.
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Read at arXiv cs.CL