A Dynamic Multi-Factor Authentication and RLWE-Based Spatio Temporal Mechanism for Securing Big Data Storage in Cloud

Authors

  • Dinesh Kumar Reddy Basani Author
  • Raj Kumar Gudivaka Author

Keywords:

Cloud Security, RLWE, Multi-Factor Authentication, Big Data, Encryption, Spatio Temporal Mechanism, Access Control, Risk Assessment, Threat Mitigation, Cybersecurity.

Abstract

Background Information: The increasing demand for cloud-based big data storage necessitates 
strong security. This paper presents a novel Dynamic Multi-Factor Authentication (DMFA) and 
Ring Learning With Errors (RLWE)-Based Spatio-Temporal Mechanism that ensures secure 
encryption, adaptive authentication, and real-time access control. Thus, the overall framework 
enhances the security, confidentiality, and reliability of access to the data against continuously 
evolving cyber threats. Objectives: With integration of DMFA and RLWE-based cryptosystems to strengthen data 
safety in cloud based big data stores. The focus lies on the minimalization of unintended access, 
with enhancing key management features, dynamic authenticity, and quality enforcement of 
spatiotemporal security policies through improving the risks relating to data leaks and cyber- 
crimes. 
Methods: Mechanism proposed: the mechanism proposed has the integration of quantum
resistant security through RLWE-based encryption, coupled with DMFA adaptive 
authentication. It also allows data coming from analytics with regards to devices, behavior, and 
location for dynamically adjusting permissions pertaining to accesses; hence, a spatio-temporal 
risk model that enables data access control with real-time mitigation in cloud storage. 
Empirical Results: The framework attains 97.8% authentication accuracy, reduces 
unauthorized access attempts by 46%, and enhances the efficiency of encrypting data by 38% 
compared to the traditional models. Results demonstrate improvement in security, 
performance, and resilience in cloud-based big data storage environments. 
Conclusion: DMFA with RLWE-based encryption makes cloud data highly secure through 
confidentiality, dynamic access control, and strong authentication. The future improvements 
are on blockchain-based logging, AI-driven anomaly detection, and post-quantum 
cryptographic techniques for securing the cloud storage system further

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Published

2026-02-08