Two-Phase Privacy Preserving Big Data Hybrid Clustering for Multi-Party Data Sharing
Keywords:
Big data, clustering, multiparty communication, partitioned database, privacy-preserving, secure channelAbstract
In this study, we take on the challenge of private data clustering. Performing a clustering technique on the union of datasets held by many parties without disclosing any further information is a scenario that has been investigated. This issue, an instance of protected multi-party computing, may be addressed using existing protocols. DBSCAN and K-Medoid apply to all data types and produce clusters identical to conventional ones, while other clustering methods are only relevant to certain kinds of data. As its name implies, DBSCAN and K-Medoid are algorithms best suited for use with a single database. In this study, we propose a method for determining the separation of data points when the information is split across two servers. This study proposes a novel method, a modified version of the privacy-preserving hybrid clustering algorithm that may be used on data sets that have been vertically and horizontally partitioned and are spread over numerous nodes in a network. The results of the experiments showed that the new technique outperformed the old ones.
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