Hybrid privacy preserving clustering for big data while ensuring security
by T.P. Pushphavathi; P.V.R. Murthy
International Journal of Cloud Computing (IJCC), Vol. 10, No. 4, 2021

Abstract: Big data is a recent technology in modern generation. Data are important information and analysed to ensure proper security. Data clustering is a fundamental technique in knowledge discovery and data engineering. Clustering a data using different algorithms is wildly used in various applications, such as soft computing, mobile computing and medicine. The traditional probabilistic clustering using c-means (PCM) algorithm is used in image analysis, and high dimensional data. However, it is difficult for PCM to produce a better result for clustering big data, mainly for heterogeneous data, since it is initially designed for small structured datasets. To contest this problem, it proposes a high-order PCM algorithm (HOPCM) for big data clustering by optimising the objective function in the tensor space. Further, we designed and developed a privacy preserved distributed HOPCM method based on MapReduce for very large amounts of heterogeneous data. The proposed approach of privacy preserving c-means (PPCM) combined with Brakerski-Gentry-Vaikuntanathan (BGV) shows better results for data security. Finally we compared the performance analysis of high-order PCM algorithm (HOPCM) with distributed high-order PCM algorithm (DHOPCM) and privacy preserving c-means (PPCM). The experimental results showed that HOPCM directs efficiently cluster a large number of heterogeneous data to provide security for private data.

Online publication date: Mon, 29-Nov-2021

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