Enabling cloud computing in emergency management systems

Emergencies, including both natural and man - made disasters, increasingly pose an immediate threat to life, health, property, and environment. For example, Hurricane Katrina, the deadliest and most destructive Atlantic tropical cyclone of the 2005 Atlantic hurricane season, led to at least 1,883 people's death and an estimated loss of - 108 billion property. To reduce the damage by emergencies, a wide range of cutting-edge technologies on medicine and information are used in all phases of emergency management. This article proposes a cloud-based emergency management system for environmental and structural monitoring that utilizes the powerful computing and storage capability of datacenters to analyze the mass data collected by the wireless intelligent sensor network deployed in civil environment. The system also benefits from smartphone and social network platform to setup the spatial and population models, which enables faster evacuation and better resource allocation.

Anonymous credential-based access control scheme for clouds

In the cloud, data is usually stored in ciphertext for security. Attribute-based encryption (ABE) is a popular solution for allowing legal data users to access encrypted data, but it has high overhead and is vulnerable to data leakage. The authors propose an anonymous authorization credential and Lagrange interpolation polynomial-based access control scheme in which an access privilege and one secret share are applied for reconstructing the user's decryption key. Because the credential is anonymously bounded with its owner, only the legal authorized user can access and decrypt the encrypted data without leaking any private information.

Analysis and prediction of major blood proteins based on their amino acid and dipeptide composition

A method has been developed for predicting blood proteins using the SVM based machine learning approach. In this prediction method a two-step strategy was deployed to predict blood proteins and their subclasses. We have developed models of blood proteins and achieved the maximum accuracies of 90.57% and 91.39% with Matthews correlation coefficient (MCC) of 0.89 and 0.90 using single amino acid and dipeptide composition respectively. Furthermore, the method is able to predict major subclasses of blood proteins; developed based on amino acid (AC) and dipeptide composition (DC) with a maximum accuracy 90.38%, 92.83%, 87.41%, 92.52% and 85.27%, 89.07%, 94.82%, 86.31 for albumin, globulin, fibrinogen, and regulatory proteins respectively. All modules were trained, tested, and evaluated using the five-fold cross-validation technique.

Cloud security

Cloud computing systems and services have become major targets for cyberattackers. To provide strong protection of cloud platforms, infrastructure, hosted applications, and data stored in the cloud, we need to address the security issue from a range of perspectives-from secure data and application outsourcing, to anonymous communication, to secure multiparty computation. This special issue on cloud security aims to address the importance of protecting and securing cloud platforms, infrastructures, hosted applications, and data storage.