# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 5 Potential Applications and Future Work
### Our Minimal Empirical Demonstration  
Although this paper is primarily conceptual, we conducted an updated experiment to demonstrate the feasibility and impact of SecureLLM's differential privacy (DP) module on both the performance and membership inference resistance. In this new trial, we fine tuned a smaller but more DP-friendly model (DistilGPT-2) on a 1000-sample subset of the WikiText-2 dataset for two epochs. We compared a baseline (no privacy) model against a DP-enabled variant that leveraged Opacus with a moderate noise multiplier (0.5). Our goal was to observe how DP affected the training overhead, model utility, and vulnerability to membership inference attacks. **Key Observations:**  
* _Training time and memory overhead:_ The baseline model required 84.2 s to complete two epochs, whereas the DP variant took 124.5 s--an approximately 47.9% increase in the total training time. The memory usage increased by about 25% under DP due to per-sample gradient tracking. * _Utility (perplexity):_ The baseline achieved a test perplexity of 39.8, while the DP-enabled model's perplexity rose slightly to 43.1. This indicates a modest reduction in the model's utility when privacy is enforced. * _Membership inference risk:_ The right-hand side of Figure 2 shows the distribution of the per-sample losses for the training (member) and test (non-member) samples. A simple threshold-based classifier achieved a 66.3% membership inference accuracy on the baseline model. With DP enabled, this accuracy dropped to 60.8%, demonstrating that injecting noise can weaken an attacker's ability to distinguish training data from non-training data. Although the perplexity values in this updated experiment are far lower than the extremely high values reported in our earlier proof-of-concept (where the perplexities exceeded 20k), it is important to note that we are still using a limited dataset and a small number of epochs. These settings do not reflect the performance of a fully converged, large-scale language model. Nonetheless, they confirm that applying differential privacy is technically feasible and can mitigate membership inference risks at the cost of an increased training overhead and a modest degradation in utility. Going forward, we are planning a series of larger-scale evaluations of SecureLLM in high-risk domains (e.g., healthcare, finance), where data confidentiality is paramount. We will track metrics such as the training time, model accuracy, privacy budget consumption \((\varepsilon,\delta)\), and vulnerability to more advanced membership inference methods or gradient manipulation attacks. In future studies, we also intend to benchmark SecureLLM against alternative security schemes (e.g., MPC-based methods like CrypTen) to quantify the trade  
Figure 2: (**Left**) Baseline training loss across two epochs, showing gradual convergence on the 1000-sample WikiText-2 subset. (**Right**) Per-sample loss distributions for training (members) vs. testing (non-members). The red dashed line marks the median loss threshold used in the membership inference attack. offs in latency, memory usage, and final model utility as a function of the model's size and security parameters. Short Textual Illustrations  
As shown in Table 3, we conducted prompt experiments to demonstrate the qualitative differences between the baseline and differentially private (DP) language models' outputs. These examples illustrate how DP affects text generation in practice beyond the quantitative metrics reported in our membership inference experiments. **Key observations from textual illustrations:**  
1. **Differences in coherence:** The DP-enabled outputs demonstrate reduced syntactic coherence compared to that of the baseline outputs. For example, in the prompt \"The future of secure AI is\", the baseline produces more structurally coherent text about complex systems, while the DP-enabled output contains disjointed phrases and grammatical inconsistencies. 2.