Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.

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Cloud computing provides a new paradigm of computing. It offers a scalable, manageable and huge pool of resources that can be accessed by users from anywhere anytime. It also ensures the integrity of data stored on the cloud. But ensuring the confidentiality and integrity of sensitive information is still a big challenge. To overcome this challenge, a hybrid two-phase security system for preserving the privacy of data on the cloud has been proposed. The hybrid approach combines feature extraction and encryption techniques to enhance the security of accessing data from the cloud. At first, the minutiae point has been extracted from the biometric fingerprint, locally collected from the state university in Northern India. The private key has been finalized by generating an elliptic curve using the minutiae point for achieving better encryption of fingerprint. The effectiveness of the approach has been tested in terms of similarity score, False Matching Ratio (FMR), False Non Matching Ratio (FNMR) and recognition accuracy, when applied on the local fingerprint database. The evidence of the outcomes suggests that the proposed technique ensures relatively improved security and privacy of data in the cloud system as compared to some recent state-of-art methods.

Health records of any type should be confidential, but simultaneously they should be accessible to an authorized user. In the current conventional health care system, health records are processed in the local database. Only authorized hospital staff can have access to the patient health records kept in health centers. The flow of information in this kind of system is center-restricted. One technology that can potentially overcome this problem is Blockchain. Blockchain technology allows sharing of data in a distributed way with benefits like privacy-preserving and secure access control. To achieve secure access control and privacy of electronic healthcare data (EHD), a secure framework for distributed sharing of the health record is proposed. The use of blockchain will remove the requirement of a trusted 3rd party for data storage. The framework uses searchable key attribute-based encryption (ABE) for achieving fine-grain access control. The simulation results indicate that our scheme is quite effective and secure.