# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 4 Discussion of the Implementation
### Differentially Private Inference Setup  
During inference, a small amount of calibrated noise can be added to the query embeddings and outputs to maintain privacy guarantees. The exact level of noise depends on the desired \((\epsilon,\delta)\)-DP parameters. For instance, in higher-risk domains (e.g., healthcare), more conservative (smaller) \(\epsilon\) values--often in the range of 1 to 2--may be chosen to strongly limit membership inference attacks, whereas a more lenient \(\epsilon\) value of up to 5 or 8 might be acceptable for less sensitive public applications. These ranges reflect a trade-off between utility and formal privacy assurances; further refinements can be guided by the regulatory demands or empirical risk analyses. An operator might configure a stricter or more lenient privacy budget depending on the domain--healthcare, for instance, may require stricter budgets than a public Q&A service. Monitoring the cumulative privacy loss across multiple queries is essential to prevent adversaries from exploiting repeated probing. To critically assess and justify the selection of differential privacy parameters such as \(\epsilon\) and \(\delta\), future implementations will incorporate a rigorous sensitivity analysis. This analysis will quantify how varying levels of noise injection--driven by different \(\epsilon\) and \(\delta\) settings--affect model utility metrics such as the accuracy, perplexity, and convergence time across diverse datasets and scenarios. Furthermore, theoretical bounds using advanced composition techniques [51] will be established to precisely characterize the cumulative privacy loss under realistic inference workloads. We also plan to investigate the robustness against different adversarial models, including powerful membership inference attacks leveraging multiple query probing. Through systematic examination of how utility deprivation is traded off against stronger privacy guarantees and through systematic measurement of the robustness afforded by the DP layer, we aim to assist the parameter selection for various threat models and operational limitations, enabling safer and transparent deployment decisions.  
Practically, differentially private inference can be realized by modifying the model's final output layer to add noise sampled from a Laplacian or Gaussian distribution [52]. The parameters for these distributions are derived from the \((\epsilon,\delta)\) bounds that the deploying organization is willing to accept. A more granular approach might assign different privacy budgets per user session, ensuring that users with especially sensitive queries (e.g., medical diagnoses) receive higher levels of protection.  
In some cases, organizations may opt for \"privacy on demand\", where the noise levels increase for specific query types flagged as high-risk (e.g., queries involving personal identifiers). This selective noise injection can preserve the model's accuracy for general queries while bolstering the protection in domains requiring stricter regulatory scrutiny [53]. Such an approach necessitates robust monitoring to track the overall privacy consumption, often implemented via a \"privacy accountant\" component within the inference pipeline.  
If an organization anticipates high query volumes, caching mechanisms or semantic search layers can reduce repeated computations while still applying differential privacy. Specifically, queries that map to similar embedding vectors can be grouped, with noise introduced at the group level. This lowers the computational overhead but demands a careful design to avoid inadvertently increasing the membership inference risks for users who share similar query patterns.