Differentially Private AI for IoT Device Anomaly Detection in Smart Homes

Smart home IoT devices are multiplying rapidly, creating both security vulnerabilities and privacy challenges for users. These devices continuously generate operational data that may expose sensitive information about household activities if improperly handled. Conventional IoT anomaly detection approaches typically require direct access to device data, risking user privacy during both training and inference. We present a privacy-preserving framework that integrates differential privacy into deep learning-based security monitoring for smart home systems. Our approach trains a binary classifier using differentially private stochastic gradient descent with moments accountant analysis to provide formal privacy guarantees. The proposed approach is evaluated across six privacy configurations using a realistic synthetic dataset consisting of 5,000 samples and 20 device-level features. Experimental results demonstrate a clear trade-off between privacy protection and detection performance. While non-private training achieves higher accuracy, models trained with strong privacy guarantees retain acceptable detection capability. Further analysis shows that privacy constraints primarily affect anomaly detection sensitivity rather than false alarm rates. Based on these findings, the paper provides practical recommendations for selecting privacy parameters across consumer, enterprise, and safety-critical Internet of Things applications. The results confirm that differentially private anomaly detection can be effectively deployed in smart home environments while balancing privacy preservation and security performance.