Privacy Protection Mechanism and Residential Security Enhancement Countermeasures for Smart Homes Combined with Internet of Things Technology

Abstract

As an important application scenario of the Internet of Things (IoT) technology, smart homes realize the interconnection and intelligent management of home devices through sensors, RFID chips and other devices. However, smart home devices face serious privacy leakage risks during data collection, transmission and processing, and sensitive data such as user behavioral data and home environment information are easily acquired and exploited by malicious attackers. In this study, a smart home privacy protection mechanism based on federated learning and differential privacy is proposed for the privacy protection of IoT devices in smart homes. The methodology adopts an adaptive hierarchical differential privacy adding noise algorithm to quantify the layer contribution by calculating the non-zero percentage of activation values and the amount of gradient change in each layer to realize the dynamic privacy budget allocation. Meanwhile, the wireless federated learning system model is established to characterize the channel properties between the base station and the user equipment using the block fading model, and combines the Gaussian and Laplace mechanisms to provide differential privacy protection. The experimental results show that on the MNIST dataset, the performance of this paper's algorithm reaches 95.16%, which is 3.56% higher than the competitive algorithm AUTO-S when the privacy budget takes the value of 10. In the smart home device recognition task, the method achieves an average adversarial rate of 98.625% in the white-box scenario and 89.39% in the black-box scenario. The conclusion shows that the privacy-preserving mechanism can effectively protect user privacy while ensuring the usability of the model, which provides reliable security for the smart home system and has good practicality and popularization value.