ZERO-TRUST LOCAL GEOFENCING FOR CHILD SAFETY
Shyamala Devi, N. (2026) ZERO-TRUST LOCAL GEOFENCING FOR CHILD SAFETY. International Journal of Engineering Development and Research, 14 (2): IJEDR26029. pp. 89-96. ISSN ISSN: 2321-9939
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Abstract
In the modern era of smart connectivity, ensuring the safety of children while they are outdoors is a primary concern for parents. While several GPS-based tracking applications exist, most rely on centralized servers that store sensitive location data in plain text, creating significant privacy risks and "single point of failure" vulnerabilities. This project proposes a Zero-Trust Local Geofence for Child Safety Ecosystem, a robust mobile solution designed to provide proactive security without compromising data privacy. The system utilizes a Two-App Architecture: a "Monitor" module for parents and a "Tracker" module for children. Unlike traditional apps, this project implements a Zero-Trust Security Model, where location coordinates are encrypted using AES-256 bit encryption on the child's device before being transmitted to the cloud. Child wants to carry a mobile. Parents can make the mobile as in stealth mode, it is invisible mode only not other apps will open like gaming and social media only Tracker app in that mobile for tracking purpose. Parents secretly put a mobile phone in their child's school bag without them knowing. Only the paired parent device holds the unique decryption key, ensuring that even the database administrators cannot access the child's movements. A core feature is Dynamic Geofencing, which allows parents to establish virtual "Safe Zones" on a map. The system utilizes "Local Boundary Computing," where the child's device independently verifies its position against these zones. If a breach occurs—such as a child leaving the school perimeter—an instant, high-priority alert is pushed to the parent’s device via Firebase Cloud Messaging. By combining Stealth Mode operations, Real-time GPS synchronization, and End-to-End Encryption, this project offers a high-tech, private, and proactive safety net for the next generation
| Item Type: | Article |
|---|---|
| Subjects: | Computer Science Engineering > Automated Machine Learning |
| Domains: | Computer Science Engineering |
| Depositing User: | Mr IR Admin |
| Date Deposited: | 10 May 2026 12:06 |
| Last Modified: | 10 May 2026 12:06 |
| URI: | https://ir.vistas.ac.in/id/eprint/13783 |
