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Scalable Anomaly Detection for Large-Scale Heterogeneous Data in Cloud Using Optimal Elliptic Curve Cryptography and Gaussian Kernel Fuzzy C-Means Clustering

In most systems, a smart functionality is enabled through an essential vital service such as detecting anomalies from complex, large-scale and dynamic data. However, ensuring the privacy and security for the cloud data is the most crucial and challenging task in the present world. Moreover, it is important to safeguard the security of sensitive data and its privacy from unauthorized parties who are trying to access the data. Therefore, to accomplish this task, several encryption, decryption and key generation mechanisms were introduced in the existing works for privacy preserving in cloud platform. But, there still remain open issues such as increased communication overhead, reduced security and increased time consumption. Also, these existing works followed the symmetric key cryptographic mechanism for privacy preservation of data; hence, a single secret key is shared by several users for accessing the original data. Due to this fact, a high security risk arises and it allows unauthorized parties to access the data. Thus, this work introduces a cloud-based privacy preserving model for offering a scalable and reliable anomaly detection service for sensor data through holding the benefits of cloud resources. Also, this paper aims to impose a newly developed Elliptic Curve Cryptography-based Collective Decision Optimization (ECDO) approach over the proposed framework for improving the privacy and security of the data. Furthermore, to perform the data clustering computation we used the Gaussian kernel fuzzy [Formula: see text]-means clustering (GKFCM) algorithm within the cloud platform, especially for data partitioning and to classify the anomalies. Thus, the computational difficulties are limited by adopting this suitable privacy preserving model which collaborates a private server and a set of public servers through a cloud data center. Moreover, on encrypted data the granular anomaly detection operations are performed by the virtual nodes executed over public servers. Experimental validation was performed on four datasets resulting from Intel Labs publicly available sensor data. The experimental outcomes demonstrate the ability of the proposed framework in providing high anomaly detection accuracy without any degradation in data privacy.