A Threshold Cryptography and Risk-Adaptive Access Control Mechanism for Enhancing Data-Centric Security in Cloud -Based Big Data Storage

Abstract

Background Information: As the use of cloud-based big data storage increases, maintaining strong data-centric security has become increasingly important. Conventional access control methods frequently fall short in the face of ever-changing and dynamic threats. To protect sensitive data while guaranteeing effectiveness and scalability in cloud environments, sophisticated strategies like threshold
cryptography and risk-adaptive access control are crucial. Objectives: This study suggests a unique security paradigm that combines risk-adaptive access control with threshold cryptography. In order to ensure safe and scalable cloud-based large data storage systems, the goal is to improve data confidentiality, minimise unwanted access, and maximise resource efficiency. Methods: To dynamically evaluate and manage risks, the model makes use of risk-adaptive access control and threshold cryptography for safe key distribution. For complete data-centric security in cloud-based systems, a multi-layered encryption technique and contextual risk assessment are used. Empirical Results: Results show that the model outperforms current security measures for cloud-based big data storage, with notable improvements in accuracy (96%), scalability (150 TPS), decreased false positive rates (2%), and optimised resource economy.
Conclusion: The suggested strategy successfully improves cloud data security by combining adaptive risk- based controls with cutting-edge encryption techniques. To increase the model's applicability, future studies will investigate AI-driven risk prediction, IoT interoperability, and edge computing optimisation.