PRIVACY PREVENTION OF BIG DATA APPLICATIONS: A SYSTEMATIC LITERATURE REVIEW
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Date
2021
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UMT, Lahore
Abstract
Big data refers to data collections that are so vast or complicated that standard data processing programs cannot handle them. The quantity of data created by the internet, social networking sites, sensor networks, healthcare apps, and many other organizations is rapidly rising as a result of recent technological advancements. Big data analytics is a term used to describe the process of investigating huge volumes of complicated data in order to uncover hid-den patterns or find hidden relationships. This research focuses on privacy and security concerns in big data. This work also covers the encryption techniques by taking existing methods such as differential privacy, k-anonymity, T-closeness and L-diversity. A number of privacy-preserving techniques have been created to safeguard privacy at various phases of a large data life cycle (for example, data production, storage, and processing). The purpose of this research is to offer a comprehensive analysis of the privacy preservation techniques in big data, as well as to explain the problems for existing systems. IEEExplore, MDPI, Science Direct, SAGE and Springer were searched with the following search terms Data protection prevention, Big Data analysis, cybercrime, safety and cyber security. The advanced repository option was utilized for the search by the use of the following in the search: "Cyber security” OR “Cybercrime”) AND ((“privacy prevention”) OR (“Big data applications”)), in order to adjust the results.