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New framework encrypts sensitive tokens for LLMs in healthcare

By PulseAugur Editorial · [2 sources] ·

Researchers have developed HERALD, a new framework for privacy-preserving clinical deployment of large language models. HERALD encrypts only sensitive tokens within clinical data, rather than the entire dataset, to reduce computational and communication overhead. This approach aims to maintain model utility while protecting sensitive health information during transmission and processing. AI

IMPACT Enables more secure and practical use of LLMs in sensitive healthcare applications by balancing privacy with utility.

RANK_REASON The cluster contains an academic paper detailing a new technical framework for LLMs.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework encrypts sensitive tokens for LLMs in healthcare

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Farhan Sheth, Ziyuan Yang, Yongying Lan, Si Yong Yeo ·

    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 leaka…

  2. arXiv cs.CL TIER_1 English(EN) · Si Yong Yeo ·

    Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models

    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 …

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