Adaptive Differential Privacy for Protecting User Confidential Information on Android Devices

Document Type : Research Paper

Authors

1 PhD Scholar, Department of CSE, SSET, Sharda University, Greater Noida, U.P. India.

2 Prof., Department of CSE, SSET, Sharda University, Greater Noida, U.P., India.

10.22059/jitm.2025.102933

Abstract

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.

Keywords

References

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Journal of Information Technology Management
Volume 17, Special Issue
2025
Pages 155-167

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