Author(s):
1. V. Devi Satya Sri:
Department CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
2. Srikanth Vemuru:
Department CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India-
Abstract:
Machine learning has been supporting real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also, this cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers to store medical records or machine learning patterns for decision making. Due to high Uncertain computational memory and time, these cloud systems require an efficient data security framework to provide strong data access control among the multiple users. In this work, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases' i.e. data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for Uncertain data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering, and security framework has better computational efficiency than the conventional approaches on large medical decision patterns.
Page(s):
136-152
DOI:
DOI not available
Published:
Journal: International Journal of Communication Networks and Information Security, Volume: 14, Issue: 1, Year: 2022
Keywords:
Cloud Computing
,
Medical Patterns
,
Cloud Uncertain Data Security
,
Data Partitioning
,
MultiUser Data Access
References:
References are not available for this document.
Citations
Citations are not available for this document.