选择性令牌级密码学编辑用于大型语言模型的隐私保护临床部署
Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models
AI总结：
提出HERALD框架，通过令牌级密码学编辑仅加密敏感令牌，在保护隐私的同时保持下游模型效用，在分类和医疗问答任务上接近明文性能。
摘要：
尽管大型语言模型（LLMs）越来越多地用于临床应用，但许多现有流程需要将原始敏感健康信息发送到远程服务器进行处理，这增加了隐私泄露的风险。缓解这种风险的一种自然方法是在传输前对数据进行加密。然而，加密整个数据集等直接解决方案会带来巨大的计算、对齐和通信开销，使得大规模实际部署不可行。为了在保护隐私的同时保持可用性，我们提出了通过自适应语言分解的医疗加密与编辑（HERALD），这是一个令牌级密码学编辑框架，通过仅加密敏感令牌同时保留上下文以供下游模型使用，实现了这种平衡。HERALD结合医学命名实体识别器（NER）与基于词性（POS）的策略来选择候选令牌，执行目标词形还原以稳定表面形式，并将每个受保护令牌替换为包裹在显式分隔符中的确定性密文。值得注意的是，HERALD是模型无关的，完全在客户端运行，确保敏感内容在存储、传输和处理过程中保持加密，无需更改下游模型。我们在公开数据集上对分类和医疗问答（MQA）任务评估了HERALD。在不同任务中，实验表明完全安全的基线遭受显著的效用损失，而HERALD始终恢复接近明文的性能。总体而言，HERALD提供了一种新颖的利用流程。
英文摘要：
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 maintaining usability, we present Healthcare Encryption & Redaction via Adaptive Linguistic Decomposition (HERALD), a token-level cryptographic redaction framework designed to achieve this balance by encrypting only sensitive tokens while preserving the surrounding context for downstream model utility. HERALD combines medical named-entity recognizer (NER) with part-of-speech (POS) driven policies to select candidate tokens, performs targeted lemmatization to stabilize surface forms, and substitutes each protected token with a deterministic ciphertext wrapped in explicit delimiters. Notably, HERALD is model-agnostic and operates entirely on the client side, ensuring that sensitive content remains encrypted throughout storage, transmission, and processing without requiring changes to downstream models. We evaluated HERALD on both classification and medical question answering (MQA) tasks on public datasets. Across different tasks, experiments illustrate that fully secured baselines suffer significant utility loss, whereas HERALD consistently recovers performance close to plaintext. Overall, HERALD provides a novel utilization pipeline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
