An improved dynamic polynomial integrity based QCP-ABE framework on large cloud data security

摘要

With the exponential growth of cloud data and network services, the computational resources and cloud data security has become one of the interesting research area of real-time cloud environment. Different types of cloud services are integrated in various domain applications such as defense, e-health, clinical databases etc, for data storage and resource computing. Attribute based encryption is a public key cryptographic algorithm that allows the cloud users to provide more security to the cloud data in the cloud storage services. Most of the traditional attribute based encryption techniques are applied on small datasets to generate constant size cipher text using limited computing resources. In the existing attribute based techniques, most of the attributes are considered as textual information and static values for key generation, data encryption and decryption process. To overcome these issues, a novel dynamic chaotic map based hashing is implemented to improve the security of the quantum based CP-ABE model. In the proposed model, user’s attribute are secured using the dynamic chaotic map function for key initialization, data encoding and decoding process. In this model, both structured and unstructured large medical data are taken as input for integrity verification and encryption process. Practical simulation results show that the presented model has better accuracy in terms of cloud data encryption and decryption time and computed memory compared to the existing attribute based encryption and decryption techniques.