Personal Data Leak Detection System

Abstract

The increasing number of data breaches and unauthorized data exposures has raised serious concerns regarding personal data security in the digital era. Sensitive user information, including email credentials, personal identifiers, and biometric data, is frequently exposed due to insecure systems, large-scale data breaches, and improper data handling practices. Such exposures often remain unnoticed by users, leading to significant privacy risks and potential misuse of personal information. This paper proposes an AI-based Personal Data Leak Detection System designed to identify and analyze potential data leaks associated with individual users. The system integrates intelligent data processing techniques, biometric analysis, and pattern recognition methods to detect exposed information across structured and unstructured datasets. Facial recognition models are utilized to identify potential biometric leaks, while additional analytical modules evaluate user-related data exposure and associated risks. The system is implemented using a Flutter-based frontend and a FastAPI backend, with Firebase authentication ensuring secure and isolated user access. Experimental observations indicate that the proposed system effectively detects user-related data leaks and enhances user awareness regarding privacy risks. The results demonstrate the potential of AI-driven approaches in strengthening digital privacy and improving cybersecurity mechanisms.

Introduction

The rapid expansion of digital technologies and online services has significantly increased the collection and storage of personal data, leading to growing concerns about data breaches, unauthorized access, and privacy violations. Traditional security measures such as encryption and access control help prevent breaches but are often ineffective in detecting already leaked data. Existing leak detection systems are generally limited, relying on manual checks or predefined databases and lacking advanced capabilities for analyzing biometric information or manipulated media.

This paper proposes an AI-based Personal Data Leak Detection System that integrates multiple intelligent technologies to identify and analyze potential personal data exposure. The system combines data leak detection, biometric recognition, and media verification into a unified platform. Using artificial intelligence techniques such as facial recognition, pattern analysis, natural language processing, and deep learning, the system can detect exposed credentials, compromised biometric data, and manipulated or AI-generated media.

The proposed framework allows users to securely provide email identifiers, facial images, videos, and media files for analysis. Facial recognition models generate feature embeddings that are compared with stored representations to identify possible biometric leaks. Data analysis modules examine structured and unstructured datasets to detect exposed personal information, while media analysis tools identify synthetic or altered content. To ensure privacy and consent, biometric inputs are restricted to real-time capture rather than uploaded gallery images.

The system architecture consists of five layers: User Interface, Authentication, Processing, Detection, and Data Storage. The frontend is developed using Flutter, while Firebase Authentication ensures secure user access and data isolation. The backend is implemented with FastAPI, providing efficient processing and secure communication. Detection modules perform facial recognition, leak analysis, and deepfake detection, while data is securely stored using SQLite and Firebase services.

Implementation results demonstrate that the system can effectively detect exposed credentials and personal information by integrating external breach databases such as Have I Been Pwned (HIBP) with internal datasets. The combination of AI-driven biometric analysis, data pattern recognition, and media verification provides a comprehensive and scalable approach to personal data protection. Overall, the proposed system enhances user awareness, improves detection accuracy, and offers a secure, user-centric solution for identifying and responding to personal data leaks.

Conclusion

This paper presented an AI-based Personal Data Leak Detection System designed to identify and analyze potential data exposure associated with individual users. The system integrates techniques such as biometric recognition, data pattern analysis, and media verification to provide a comprehensive approach to personal data security. The results demonstrate that the system is capable of detecting user-related data leaks using available data sources while maintaining secure and user-specific data handling. Future work can focus on improving detection accuracy, expanding data sources, and enabling real-time monitoring capabilities. Additional enhancements such as advanced user consent mechanisms and performance optimization can further strengthen the system for large-scale deployment

References

[1] J. Deng et al., “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” in Proceedings of CVPR, 2019. [2] H. Nguyen et al., “Deep Learning for Deepfakes Creation and Detection,” arXiv preprint arXiv:1909.11573. [3] M. Abadi et al., “Deep Learning with Applications in Security and Privacy,” Communications of the ACM. [4] A. Narayanan et al., “Machine Learning Approaches for Data Leak Detection,” IEEE Security & Privacy. [5] Have I Been Pwned, “Data Breach Search API,” [Online]. Available: https://haveibeenpwned.com/API. [6] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, 2015.

Copyright

Copyright © 2026 Asish Mundappallil, Deepak Ravi, Kevin Sebastian. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.