Fog-Enhanced Personalized Privacy-Preserving Data Analysis for Smart Homes

Abstract

The proliferation of Internet of Things (IoT) devices has led to a surge in data generation within smart home environments. This data explosion has raised significant privacy concerns and highlighted a lack of user-friendly controls. Consequently, there is a pressing need for a robust privacy-enhancing mechanism tailored for smart homes, safeguarding sensitive data from a user-centric perspective. In this article, we introduce the Fog-enhanced Personalized Differential Privacy (FEPDP) model, which utilizes the distributed nature of fog computing to improve data processing efficiency and security in smart homes. Specifically, the personalization, as a key feature of FEPDP, is manifested through an array of user-driven policy specifications, enabling home users to specify secret and privacy specifications for their personal data. These specifications not only enhance control over personal data but also align with the heterogeneous nature of smart home environments. Subsequently, aligned with fog-based smart home architecture, we propose two policy-driven partitioning mechanisms that utilize threshold partitioning based on dynamic programming to effectively implement FEPDP. Finally, comprehensive theoretical analysis and experimental validation across various statistical analysis tasks and datasets confirm that FEPDP achieves a superior privacy-utility trade-off for smart home data by leveraging non-sensitive data and fog-based partitioning.