Android Security

The widespread adoption of Android phones has heightened concerns about user privacy. This research presents an Adaptive Privacy Management System (APMS) that integrates Machine Learning (ML) models with Differential Privacy techniques to enhance privacy protection. The APMS monitors application behavior and employs ML algorithms to detect anomalies and enable context-aware privacy enforcement. Differential Privacy ensures that sensitive data remains protected through the addition of noise and privacy-preserving computations. Experimental results demonstrate that the APMS achieves a 92.5% accuracy rate in detecting the privacy leakage. The anomaly detection model, using Random Forest, shows high accuracy (92.5%), recall (89.5%), and precision (73.9%), effectively identifying both normal and anomalous behaviors. Additionally, the impact of noise on data utility, controlled by the privacy budget (ε), is manageable. The results show that APMS is a robust system for safeguarding user confidential information, contributing to a more secure and privacy-centric Android ecosystem.