Empirical evaluation of clustering-based privacy preserved big data

The term 'privacy-preserving data publishing' (PPDP) refers to a concept that offers several tools and methods for protecting data privacy while the data is published over the Internet. The significant strategies utilised in the field of privacy-preserving data mining or data publishing are data anonymisation, data randomisation, and cryptography. The major purpose of this survey is to determine clustering-based privacy preserved big data. Based on the literature review classification, present methods are categorised into cluster-based methods, anonymisation-based methods, security-based methods, and algorithm-based methods. This survey is established by considering the used dataset, toolsets used, published year, performance metrics, classification of methods, etc. The research gaps and issues part of the current review papers includes a comprehensive description of the shortcomings. Therefore, the part on research needs is considered an inspiration for the continued development of big data with privacy protection.