EdgeSanitizer: Locally Differentially Private Deep Inference at the Edge for Mobile Data Analytics

Deep neural networks have been widely applied in various machine learning applications for mobile data analytics in cloud. However, this approach introduces significant data challenges, because the cloud operator can perform deep inferences on the available data. Recent advances in edge computing have paved the way to more efficient and private data processing at the edge of the network for simple tasks and lightweight models, but challenges still remain in building efficient complex models (e.g., deep learning) for edge computing. To tackle these issues, we propose EdgeSanitizer, a deep inference framework-based edge computing with local differential privacy for mobile data analytics. EdgeSanitizer leverages deep learning model to conduct data minimization and obfuscates the learned features by adaptively injecting noise, thereby forming a new protection layer against sensitive inference. We evaluate its performance in terms of data privacy and utility through theoretical analysis and experimental evaluation. The theoretical analysis proves that EdgeSanitizer can provide provable privacy guarantees with a large improvement in utility. And the experimental results demonstrate the robustness of our approach against sensitive inference, as well as its applicability on resource-constrained edge devices.