# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 2 Background
### Existing Solutions
This viewpoint motivates the SecureLLM framework, which leverages lightweight cryptographic approaches, decentralized protocols, and differentially private inference to secure LLM training, fine-tuning, and real-world inference scenarios. In contrast to solutions that focus solely on one technique (e.g., differential privacy during training [8] or homomorphic encryption for raw data [9]), SecureLLM addresses every phase of the LLM lifecycle (training, fine-tuning, and inference) through a combination of lightweight cryptography, MPC-based decentralized updates, and differential privacy for inference. This synergy reduces the attack surface beyond what any single method can achieve alone. Moreover, SecureLLM strategically balances performance and security by minimizing the cryptographic overhead and selectively applying noise injections, thereby achieving a lower latency than that of full-homomorphic-encryption-based systems while still offering stronger privacy guarantees than the standard federated or enclave-only approaches. For instance, secure enclaves [10] protect computations in trusted hardware but do not inherently mitigate membership inference from public queries. Federated approaches keep the data local yet risk exposing plaintext gradients. By layering cryptography, decentralized fine-tuning, and DP-based inference in one unified framework, SecureLLM attains a more holistic security posture. Although the exact performance metrics can vary across implementations, a principal advantage of SecureLLM lies in avoiding the steep computational costs of fully homomorphic encryption (FHE) while still restricting the exposure of plaintext data and gradients more effectively than federated-only meth \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Technique** & **Focus** & **Advantages** & **Limitations** \\ \hline
**Differential Privacy** & \begin{tabular}{l} Obfuscating training data \\ \end{tabular} & \
\begin{tabular}{l} Protects individual records, \\ mathematically grounded \\ \end{tabular} &
\begin{tabular}{l} Performance trade-offs, \\ noise tuning can be tricky \\ \end{tabular} \\
**Homomorphic Encryption** & \begin{tabular}{l} Computing on \\ encrypted data \\ \end{tabular} & \
\begin{tabular}{l} Preserves confidentiality of \\ inputs and outputs \\ \end{tabular} &
\begin{tabular}{l} High computational cost \\ for large-scale LLMs \\ \end{tabular} \\
**Secure Enclaves** & \begin{tabular}{l} Hardware-based isolation \\ \end{tabular} & \
\begin{tabular}{l} Hardware-supported TEE, \\ strong data isolation \\ \end{tabular} &
\begin{tabular}{l} Requires specialized \\ hardware, limited memory \\ Communication overhead, \\ complex key management \\ \end{tabular} \\
**SecureLLM** & \begin{tabular}{l} Integrated cryptography + DP \\ \end{tabular} & \
\begin{tabular}{l} The lightweight encryption, \\ DP at inference, \\ decentralized fine-tuning \\ \end{tabular} &
\begin{tabular}{l} Must balance overhead, \\ noise, and compliance \\ simultaneously \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the existing solutions for LLM security and privacy relative to the proposed method. ods. Moreover, our small-scale test (Section 5.5) shows how even a partial SecureLLM deployment reduces the membership inference vulnerabilities, underlining its value over narrowly focused solutions. In recent years, a variety of works have begun to construct composite frameworks that integrate multiple privacy protection strategies. For example, techniques that hybridize federated learning with differential privacy, as well as secure multi-party computation, have produced reassuring results at finding a balance among privacy guarantees and model utility. However, these hybrid methods often face challenges in scaling to the size and complexity of modern large language models, or they incur a significant computational overhead.