Abstract
Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data. This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.
Highlights
Big data [1, 2] refers to data sets that are so large or complex that traditional data processing applications are not sufficient
Conclusion and future work Big data [2, 68] is analysed for bits of knowledge that leads to better decisions and strategic moves for overpowering businesses
We have investigated the privacy challenges in big data by first identifying big data privacy requirements and discussing whether existing privacypreserving techniques are sufficient for big data processing
Summary
Big data [1, 2] refers to data sets that are so large or complex that traditional data processing applications are not sufficient. De‐identification De-identification [29, 30] is a traditional technique for privacy-preserving data mining, where in order to protect individual privacy, data should be first sanitized with generalization (replacing quasi-identifiers with less particular but semantically consistent values) and suppression (not releasing some values at all) before the release for data mining. T‐closeness It is a further improvement of l-diversity group based anonymization that is used to preserve privacy in data sets by decreasing the granularity of a data representation This reduction is a trade-off that results in some loss of adequacy of data management or mining algorithms in order to gain some privacy.
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