Deploying machine learning models on edge devices raises significant privacy concerns, as sensitive user data is processed locally. This article explores a differential privacy approach where Laplacian noise is injected into the model's output layer before returning predictions. The author provides a rigorous analysis of the accuracy-privacy tradeoff, showing how different noise scales (epsilon values) impact both the privacy budget and the model's predictive performance. Key findings include the identification of optimal noise levels that maintain acceptable accuracy while providing strong privacy guarantees. The design is particularly relevant for applications like health monitoring, personal assistants, and financial tools running on smartphones or IoT devices. The analysis also covers practical implementation considerations, such as noise calibration for different model architectures and the impact on latency. This work provides a valuable reference for engineers building privacy-preserving edge AI systems.
A detailed analysis of differential privacy for on-device inference using Laplacian noise, quantifying the accuracy-privacy tradeoff for edge AI deployments.