Cloud Computing Storage Backup and Recovery Strategy Based on Secure IoT and Spark

Spatial data occupies a large proportion of the large amount of data that is constantly emerging, but a large amount of spatial data cannot be directly understood by people. Even a highly configured stand-alone computing device can hardly meet the needs of visualization processing. In order to protect the security of data and facilitate for users the search for data and recover by mistake, this paper conducts a research on cloud computing storage backup and recovery strategies based on the secure Internet of Things and Spark platform. In the method part, this article introduces the security Internet of Things, Spark, and cloud computing backup and recovery related content and proposes cluster analysis and Ullman two algorithms. In the experimental part, this article explains the experimental environment and experimental objects and designs an experiment for data recovery. In the analysis part, this article analyzes the challenge-response-verification framework, the number of data packets, the cost of calculation and communication, the choice of Spark method, the throughput of different platforms, and the iteration and cache analysis. The experimental results show that the loss rate of database 1 in the fourth node is 0.4%, 2.4%, 1.6%, and 3.2% and the loss rate of each node is less than 5%, indicating that the system can respond to applications.

Robust Security with Strong Authentication in Mobile Cloud Computing based on Trefoil Congruity framework

In the mobile cloud computing era, the sharing of secured large-scale data which have major challenges. From an existing quantum based security mechanism randomly chosen the photon detector which creates small length of qubits so it cannot provide much security in MCC also data storage in the cloud server doesn’t guarantees the lossless back up and data recovery as well attains more computation complex during secure access of stored data. Therefore to solve those issues a unique combination of the Trefoil Congruity framework is proposed which consist Quantum Key Fibo Privacy Approach (QKFPA) performing the quantum key generation for encrypt and decrypt the data with the aid of Fibonacci chain-slanting matrix. Based on that quantum key data is uploaded, then secured data should be stored, ultra-widely distributed data transfer mechanism does the scrambling with sorting the stored data by implementing novel HS-DRT technique that improves the lossless backup and recovery of data storage.

A Proposed Framework for Developing an Electronic Medical Record System

The deployment of an electronic medical records (EMR) raises several important issues. Those addressed in this study are the access to such a system, the satisfaction on the security and authorization protocols to follow, the awareness of backup and recovery mechanisms in place, and the appreciation of the training of the IT staff. This qualitative study took place in the natural setting of the medical units' environments. A purposive sample of 40 professionals in Greece and Oman was used. The study underlines that the patients should have access to their records, whereas for the pharmacists the professionals' views are seriously divided. Every other person's access to such a record should be restricted and recorded. The professionals are satisfied with the security level, the ICTs training, and the backup and recovery mechanism in place. They almost all admitted there is an authorization schema followed to access the EMR. The main contribution of the study is the proposal of a framework of policies and procedures for the development of such a system.

Data Backup and Recovery With a Minimum Replica Plan in a Multi-Cloud Environment

Cloud computing has become a desirable choice to store and share large amounts of data among several users. The two main concerns with cloud storage are data recovery and cost of storage. This article discusses the issue of data recovery in case of a disaster in a multi-cloud environment. This research proposes a preventive approach for data backup and recovery aiming at minimizing the number of replicas and ensuring high data reliability during disasters. This approach named Preventive Disaster Recovery Plan with Minimum Replica (PDRPMR) aims at reducing the number of replications in the cloud without compromising the data reliability. PDRPMR means preventive action checking of the availability of replicas and monitoring of denial of service attacks to maintain data reliability. Several experiments were conducted to evaluate the effectiveness of PDRPMR and the results demonstrated that the storage space used one-third to two-thirds compared to typical 3-replicas replication strategies.