Comprehensive Analysis of Privacy and Data Mining Techniques

Data analytics, also referred to as big data, is a technique for analyzing massive amounts of information in both organized and unstructured forms in order to uncover relevant patterns and trends. It is necessary to design a proper framework for the use of sensitive data, as well as assurances that sensitive data will not be breached or altered, in order to protect big data privacy. Consequently, because big da-ta systems are substantially larger in terms of both scale and velocity than regular big data systems, the privacy rules for traditional database systems do not apply to big data systems, and vice versa. The delivery of new models and algorithms to large-scale data repositories that handle developing distributed contexts such as clouds, social networks, and other similar environments is essential for data privacy preservation and data security in big data environments. The current research is aimed towards achieving this goal. As a result of the usage of data for data analysis, businesses must develop a data-sharing plan that ensures data security while also protecting individual privacy. Data sharing governance is the term used to describe this process. Because of this research, the Chain-PPDM architecture for privacy-preserving massive data sets, which makes use of block chain technology and the Inter Planetary File System, has been developed and is now in use (IPFS). For the purpose of better understanding ChainPPDM, this research adopts a data mining scenario in which a large number of people are participating in examining and detecting patterns from vast volumes of data. We used data from healthcare organizations and online food delivery firms to test and validate our method.