# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
###### Abstract  
Large language models (LLMs) have shown remarkable skills across various activities, including text generation and code synthesis. Their widespread applicability, however, raises substantial concerns about security, privacy, and possibly misuse. Of recent legislative efforts, the most notable is the proposed EU AI Act, which classifies specific AI applications as high-risk. For detailed regulatory guidance, also refer to the GDPR and HIPAA privacy rules. This paper introduces SecureLLM, a novel framework that integrates lightweight cryptographic protocols, decentralized fine-tuning strategies, and differential privacy to mitigate data leakage and adversarial attacks in LLM ecosystems. We propose SecureLLM as a conceptual security architecture for LLMs, offering a unified approach that can be adapted and tested in real-world deployments. While extensive empirical benchmarks are deferred to future studies, we include a small-scale demonstration illustrating how differential privacy can reduce membership inference risks with a manageable overhead. The SecureLLM framework underscores the potential of cryptography, differential privacy, and decentralized fine-tuning for creating safer and more compliant AI systems. large language models; security; privacy; cryptography; differential privacy; decentralized fine-tuning; EU AI Act +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-  
internal representations [5]. Moreover, recent works have demonstrated that both advanced querying techniques and model distillation strategies can be used to make membership inference attacks more effective, which brings an urgent requirement for the development of enhanced privacy protection [6]. As LLMs continue to grow in size (often surpassing hundreds of billions of parameters), adversaries can exploit novel attack surfaces to extract proprietary or user-specific information. This reality has prompted calls for a new generation of secure AI tools that integrate cutting-edge cryptography, privacy-preserving technologies, and robust distributed training protocols [7]. In this context, even a minimal proof-of-concept experiment--such as the small-scale differential privacy test reported in Section 5.5--can underscore how added security measures might affect the overhead and membership inference risks. In contrast to prior works that focused solely on one technique (e.g., differential privacy alone or purely federated training), our contribution lies in holistically integrating (1) lightweight cryptography, (2) decentralized fine-tuning using MPC, and (3) differentially private inference into a single cohesive framework tailored to large language models. By systematically layering these methods, our SecureLLM architecture aims to minimize the data leakage risks across all stages of the LLM pipeline (training, fine-tuning, and inference), rather than only at training time. This synergy, rarely treated in a unified manner in the existing literature, allows for stronger compliance with emerging regulations and a better balance between efficiency and security for real-world LLM deployments. The existing research has examined the use of techniques like differential privacy [8], homomorphic encryption [9], and secure enclaves [10] to bolster the privacy and security of LLM deployments. While all of these techniques have seen promising individual results, comprehensive frameworks that put cryptographic primitives, differential privacy, and decentralized learning strategies together for large models have been relatively unexploited. This literature gap needs to be filled due to the high stakes of LLM deployments in real-world environments that process sensitive user data, private corporate information, and confidential government communications.