# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 2 Background
### Existing Solutions  
**Differential privacy:** Differential privacy adds noise during training in a controlled manner, masking the contributions of individual training examples [24]. Over the past few years, there has been increasing interest in applying DP mechanisms to very large transformer models; for example, several works have investigated adaptive clipping strategies, techniques for distributing noise injections across data-parallel workers, and privacy budgets tailored to billion-parameter models. **Homomorphic encryption:** Full homomorphic encryption (FHE) enables computations on encrypted data without decryption [25]. Hybrid techniques have been developed that rely on partial or approximate homomorphic schemes while aiming to reduce the overhead and maintain strong security guarantees whenever raw data confidentiality is essential [26]. **Secure enclaves:** Secure enclaves are hardware constructs that protect sensitive computation from the operating system [27]. **Decentralized learning:** Recent work in federated and decentralized learning has studied how to train models collaboratively without revealing raw data. Techniques from multi-party computation allow several parties to jointly compute any function while keeping their inputs private [28]. Table 1 presents a comparision of existing solutions for LLM Security and privacy relative to the pro posed method. While each of these approaches has specific strengths, their real-world usage tends to occur in isolation, with corresponding limitations in their overall security. For instance, although differential privacy can protect training data through anonymization, it does not address adversarial actions and inference queries, nor does it cover collusion with untrustworthy entities during training [29]. Similarly, homomorphic encryption can secure raw data but may become computationally expensive for full-scale LLM operations, creating potential bottlenecks in production. Research since 2021 has intensified efforts to combine or \"stack\" these techniques to deliver multi-layered defense. One prominent thread explores the synergy between federated learning and secure enclaves: decentralized participants train local models within encrypted or protected hardware, thereby avoiding the need to share raw data with a central server [30]. Another emerging line examines how multi-party computation (MPC) protocols can be adapted to large models, particularly by compressing gradient information before encryption. These solutions remain promising, especially for corporate consortia and cross-institution collaborations, but still demand careful scheduling and key management to prevent partial data leaks. Additionally, organizations have begun to adopt \"explainable AI\" (XAI) and accountability frameworks to trace the pathways through which an LLM arrives at specific outputs [31]. Although XAI does not directly encrypt data or apply DP, it offers a supplementary safeguard: if a model inadvertently reveals sensitive content, an audit trail may help pinpoint the underlying cause (e.g., memorized examples in the training set). Nonetheless, a strong imperative remains for converged architectures that combine cryptography, differential privacy, and decentralized learning into a single system. Such frameworks must demonstrate enough adaptability to function under real-world constraints (e.g., limited GPU availability, variable network bandwidth, evolving regulations) while offering robust countermeasures against adversaries. SecureLLM arises in response to this demand, reducing the complexity and risk of ad hoc integrations. Taken together, the existing solutions illustrate both the promise and limitations of the current LLM security methods. Researchers and industry practitioners are increasingly acknowledging that only a combination of techniques--applied throughout an AI's lifecycle--can effectively safeguard against modern threats.