# Guarding Digital Privacy: Exploring User Profiling and Security Enhancements

arXiv: https://arxiv.org/abs/2504.07107 · HTML: https://mlsanity.com/paper/2504.07107/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 96 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 10 of 10)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [One Person, One Model, One World: Learning Continual User Representation without Forgetting](https://mlsanity.com/paper/2009.13724/) (2020)
- [News Recommendation with Candidate-aware User Modeling](https://mlsanity.com/paper/2204.04726/) (2022)
- [Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning](https://mlsanity.com/paper/2208.01182/) (2022)
- [ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic](https://mlsanity.com/paper/1507.00255/) (2015)
- [The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences](https://mlsanity.com/paper/1703.02090/) (2017)
- [A Smart Home is No Castle: Privacy Vulnerabilities of Encrypted IoT Traffic](https://mlsanity.com/paper/1705.06805/) (2017)
- [User Profiling Trends, Techniques and Applications](https://mlsanity.com/paper/1503.07474/) (2015)
- [AntShield: On-Device Detection of Personal Information Exposure](https://mlsanity.com/paper/1803.01261/) (2018)
- [Cleartext Data Transmissions in Consumer IoT Medical Devices](https://mlsanity.com/paper/1803.10147/) (2018)

## Abstract (arXiv)

User profiling, the practice of collecting user information for personalized recommendations, has become widespread, driving progress in technology. However, this growth poses a threat to user privacy, as devices often collect sensitive data without their owners' awareness. This article aims to consolidate knowledge on user profiling, exploring various approaches and associated challenges. Through the lens of two companies sharing user data and an analysis of 18 popular Android applications in India across various categories, including textitSocial, Education, Entertainment, Travel, Shopping and Others, the article unveils privacy vulnerabilities. Further, the article propose an enhanced machine learning framework, employing decision trees and neural networks, that improves state-of-the-art classifiers in detecting personal information exposure. Leveraging the XAI (explainable artificial intelligence) algorithm LIME (Local Interpretable Model-agnostic Explanations), it enhances interpretability, crucial for reliably identifying sensitive data. Results demonstrate a noteworthy performance boost, achieving a 75.01% accuracy with a reduced training time of 3.62 seconds for neural networks. Concluding, the paper suggests research directions to strengthen digital security measures.