# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 3 Proposed Methods: The SecureLLM Framework  
SecureLLM introduces the use of cryptography, decentralized fine-tuning, and differential privacy in a layered approach to meeting these requirements. Extending previous works on treating these components in isolation, SecureLLM has developed an integral approach to harmonization--lightweight encryption, collaborative gradient aggregation, and dynamic noise injection--all into one end-to-end pipeline, affording greater security to modern AI deployments [33]. This overall integrated design will finally enable the protection of sensitive training data and inference processes with minimal overhead and no observable degradation in the core model performance. Building on existing theory around secure distributed learning, SecureLLM's overarching design philosophy emphasizes modularity, adaptability, and end-to-end protection. By treating data security and privacy as first-class design concerns, we focus on constructing each layer in a way that can be adapted to varying hardware, data governance policies, and risk profiles. In practice, different organizations might choose to prioritize certain sub-layers or tune the parameters differently (e.g., adjusting key rotation intervals in the cryptography layer) depending on their regulatory environment or the sensitivity of their data. Another central consideration is how to balance the tension between usability and security. In older frameworks, cryptographic protocols sometimes created overhead that rendered large-scale training infeasible. SecureLLM addresses this by adopting a combination of symmetric and asymmetric cryptography that can be tailored to specific deployment scales. Similarly, differential privacy schemes have traditionally struggled with the trade-off between privacy loss and model utility, but SecureLLM's approach of distributing noise over both training and inference contexts allows for the more targeted protection of sensitive interactions. Below, we detail each of the three layers of SecureLLM, explaining each component's conceptual underpinnings and technical contributions. While the layers are presented sequentially, in practice, they can be integrated and tuned together, creating a dynamic security ecosystem that is responsive to real-time performance and compliance needs. **The integration of security layers. ** The three layers of SecureLLM are designed to work in unison: lightweight cryptography secures the data during transit, decentralized fine-tuning aggregates the encrypted gradients securely, and differential privacy at inference ensures that the final outputs are protected against membership inference. The system is engineered so that the overhead introduced by cryptographic operations is balanced by efficient MPC protocols, while the noise calibration in DP is tuned to maintain model utility. However, we are aware that stacking lightweight cryptography, fine-grained decentralization, and differential private inference has the potential for interaction effects that could influence the overall framework's effectiveness. An example is that sequential stacking can accumulate latency, especially for apps with shared ephemeral key rotations and also with decentralization gradient aggregation. Additionally, the simultaneous use of differential privacy and cryptographic measures may result in compounded noise levels, potentially degrading the model's accuracy beyond the acceptable thresholds. Furthermore, interactions between cryptographic protocols and MPC-based aggregation could inadvertently introduce unforseen vulnerabilities--such as weakened randomness due to overlapping entropy sources or compromised key exchange protocols--that could negatively impact other security layers. To reduce such integration risks, painstaking and structured parameter tuning for every layer is required.