Geofence Encryption: A Smart Sheild for Cloud

Abstract

Cloud computing has advanced data accessibilitybutintroducedserioussecurity concerns, especially regarding unauthorized access. Traditional methods relying on static credentials lack physical- context awareness, exposing sensitive data tolocation-independentthreats.Toaddress this, we propose “Geofence Encryption: A Smart Shield for Cloud,” a system that enforces access based on real-time geographic validation alongside AES-256 encryption.BuiltwithaFlaskmicroservice architecture, it restricts data decryption to authorized geofenced zones, combining spatial logic with cryptographic security. Experimental results confirm accurate geolocation enforcement, low encryption latency (~120ms), and strong resistance against spoofing and unauthorized attempts. This solution offers a dynamic, context-aware approach to secure cloud environments.

Introduction

The evolution of cloud computing has transformed digital data management, offering scalability, cost-efficiency, and global accessibility. However, this reliance on cloud services has introduced significant security challenges. Traditional security methods like passwords, MFA, and RBAC are insufficient against sophisticated threats, especially due to the lack of physical context in access control. Current models don’t adequately verify geographic origin, allowing attackers with stolen credentials to bypass location restrictions. Encryption methods protect data but do not limit decryption by location, risking data exposure and regulatory non-compliance.

To address this, the research proposes “Geofence Encryption: A Smart Shield for Cloud,” combining geolocation-based access validation with AES-256 encryption. The system only allows data decryption within defined geofenced zones, validated via real-time GPS using a Flask microservice architecture. Unauthorized access attempts trigger alerts, while an administrative dashboard manages geofences and monitors activity. This dual-layer security enforces spatial restrictions alongside encryption, enhancing trust and compliance.

The system was tested with sensor simulations and various attack scenarios, showing over 98% accuracy in geofence validation, low encryption/decryption latency, and fast alert responses. It is particularly suitable for industries with strict data residency and security requirements.

Related works discussed earlier systems integrating location-based controls or encryption, noting their limitations such as lack of post-authentication encryption, GPS inaccuracies, or latency issues. This work improves upon them by tightly coupling geofence validation with AES-256 encryption in a modular, real-time system.

Conclusion

The increasing complexity of cloud environments and the heightened risk of unauthorizeddataaccessnecessitateashift from traditional identity-based security toward more context-aware frameworks. This paper introduced Geofence Encryption: A Smart Shield for Cloud, a novel system that integrates real-time geolocation validation with AES-256 encryption to provide location-restricted, secure access to cloud resources. Unlike conventionalapproachesthatrelysolelyon usercredentials, ourproposed methodology ensures that even verified users can only access data from physically authorized zones, thus strengthening the trust and compliance posture of cloud deployments. The framework incorporates real-time IoT sensor simulations for environmental monitoring, a geofence validation engine for spatial access control, and a secure AES-256 encryption handler for safeguarding data in transit and at rest. Evaluated across various test scenarios. These outcomes directly address the problem statement outlined in the abstract—enforcing location-based access while maintaining data confidentiality and real-time observability. Lookingforward,thesystemcanbefurther enhanced by incorporating biometric validation and blockchain-backed audit logs for immutable access trails. Additionally, adaptive geofencing using machine learning can make spatial access rules context-sensitive, dynamically adjustingtousagepatternsandthreatlevels. These enhancements can significantly improve the scalability, intelligence, and trustworthiness of the platform in evolving cloud ecosystems

References

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Copyright

Copyright © 2025 B Yasmeen Naaz, Mrs. Jennifer Mary S. 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.