Multi-tier Privacy Protection for Large Language Models using Differential Privacy

The increasing integration of machine learning models into sensitive domains such as healthcare, finance, and government services has amplified concerns surrounding data privacy and the protection of personal information. A novel multi-tier differential privacy mechanism is proposed, offering a flexible and scalable solution to address these concerns through the dynamic adjustment of privacy settings based on data sensitivity. The approach involves systematically applying varying levels of noise during both the training and inference stages of Llama, ensuring that privacy guarantees are maintained while balancing model utility and performance. Experimental results highlight the effectiveness of this mechanism, showing that privacy can be preserved across different tiers, with stronger privacy levels associated with higher noise injection but also leading to noticeable trade-offs in terms of accuracy, latency, and computational resources. The evaluation demonstrated that moderate privacy settings enable a reasonable balance between performance and privacy protection, making the method adaptable for real-world applications in privacy-sensitive environments. The comparison with non-private models further demonstrated the computational overhead introduced through differential privacy mechanisms, while highlighting the feasibility of employing such privacypreserving techniques without significantly compromising the functionality of the model.