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This paper introduces an efficient and scalable cloud-based privacy preserving model using a new optimal cryptography scheme for anomaly detection in large-scale sensor data. Our proposed privacy preserving model has maintained a better tradeoff between reliability and scalability of the cloud computing resources by means of detecting anomalies from the encrypted data. Conventional data analysis methods have used complex and large numerical computations for the anomaly detection. Also, a single key used by the symmetric key cryptographic scheme to encrypt and decrypt the data has faced large computational complexity because the multiple users can access the original data simultaneously using this single shared secret key. Hence, a classical public key encryption technique called RSA is adopted to perform encryption and decryption of secure data using different key pairs. Furthermore, the random generation of public keys in RSA is controlled in the proposed cloud-based privacy preserving model through optimizing a public key using a new hybrid local pollination-based grey wolf optimizer (LPGWO) algorithm. For ease of convenience, a single private server handling the organization data within a collaborative public cloud data center when requiring the decryption of secure sensor data are allowed to decrypt the optimal secure data using LPGWO-based RSA optimal cryptographic scheme. The data encrypted using an optimal cryptographic scheme are then encouraged to perform data clustering computations in collaborative public servers of cloud platform using Neutrosophic c-Means Clustering (NCM) algorithm. Hence, this NCM algorithm mainly focuses for data partitioning and classification of anomalies. Experimental validation was conducted using four datasets obtained from Intel laboratory having publicly available sensor data. The experimental outcomes have proved the efficiency of the proposed framework in providing data privacy with high anomaly detection accuracy.

Cloud computing is a computing technology that is expeditiously evolving. Cloud is a type of distributed computing system that provides a scalable computational resource on demand including storage, processing power and applications as a service via Internet. Cloud computing, with the assistance of virtualization, allows for transparent data and service sharing across cloud users, as well as access to thousands of machines in a single event. Virtual machine (VM) allocation is a difficult job in virtualization that is governed as an important aspect of VM migration. This process is performed to discover the optimum way to place VMs on physical machines (PMs) since it has clear implications for resource usage, energy efficiency, and performance of several applications, among other things. Hence an efficient VM placement problem is required. This paper presents a VM allocation technique based on the elephant herd optimization scheme. The proposed method is evaluated using real-time workload traces and the empirical results show that the proposed method reduces energy consumption, and maximizes resource utilization when compared to the existing methods.

In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid COOT-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid COOT-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques.