@Article{cmc.2026.075985,
AUTHOR = {Ans D. Alghamdi},
TITLE = {PrivLLM-Guard: A Differentially-Private Large Language Model for Real-Time Confidential Medical Text Generation and Summarization},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {87},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v87n3/66910},
ISSN = {1546-2226},
ABSTRACT = {<i>How can AI assist doctors in generating clinical reports without compromising patient privacy?</i> This question motivates our development of PrivLLM-Guard, a novel framework for differentially private large language models (LLMs) tailored to real-time confidential medical text generation and summarization. While LLMs have shown promise in automating clinical documentation, the sensitivity of healthcare data demands rigorous privacy protections. PrivLLM-Guard addresses this need by combining advanced—differential privacy techniques with adaptive noise calibration, ensuring robust privacy guarantees without sacrificing utility. The framework integrates bidirectional transformer encoders with autoregressive decoders, further enhanced by privacy-aware attention and gradient perturbation mechanisms. Extensive experiments on three large-scale medical datasets demonstrate BLEU-4 scores of 89.7% for generation and ROUGE-L scores of 92.3% for summarization, while maintaining strict privacy budgets. The model processes 512-token sequences in real time with an average latency of 245 ms and memory usage of just 4.2 GB. Compared to state-of-the-art privacy-preserving LLMs, PrivLLM-Guard improves the utility-privacy trade-off by 15.8% and reduces computational overhead by 23.4%. Key contributions include adaptive noise injection, dynamic privacy budgeting, and an integrated privacy auditing module—collectively advancing secure and trustworthy AI deployment in clinical environments.},
DOI = {10.32604/cmc.2026.075985}
}