Real-Time Privacy Risk Detector for Android Apps

DOI

10.2991/978-94-6239-713-2_16How to use a DOI?

Keywords

Privacy policy analysis; Android security; transformer-based NLP; DistilBERT model; real-time risk detection; data privacy compliance; browser-extension monitoring

Abstract

Android applications are regularly involved with sensitive user information in the contemporary mobile ecosystem and are often regulated by privacy policies that are too long, vague, and incomprehensible to the end-user. Such ambiguity may result in the accidental consent, unauthorized data gathering, third-party surveillance, and the breach of the regulatory requirements like GDPR and CCPA. As a counter to these privacy issues, this paper has presented a prototype of a Real-Time Privacy Risk Detector of Android Apps which is an auto- mated system to analyze, categorize, and label privacy risk statements in the policies of Android apps. The system uses a deeply tuned Distil- BERT transformer model that has the ability to comprehend legal terms and identify privacy-related semantics with high precision. The backend written in FastAPI works with text or URLs, and in real-time it makes inferences related to policy segments related to sharing data, location tracking, behavioral profiling, and sensitive information usage. A special policy-fetching module will automatically fetch and analyze recent app policies of such platforms as the Google Play Store. A browser extension can be used to protect users by protecting in real time as a privacy fil- ter, determining when an app is installed and displaying an intelligent Block, Warn, or Allow. It is a unified AI-driven system that enhances the user privacy awareness, compliance tracking, and how transformer-based NLP can enable users to use mobile securely and to maintain transparent data controls.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - C. Palanivel Rajan
AU  - B. Nagalakshmi
AU  - S. Roshini
PY  - 2026
DA  - 2026/06/25
TI  - Real-Time Privacy Risk Detector for Android Apps
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 219
EP  - 233
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_16
DO  - 10.2991/978-94-6239-713-2_16
ID  - Rajan2026
ER  -