A Study on Privacy Preserving in Big Data Mining Using Fuzzy Logic Approach
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Abstract
Information security is the most acclaimed issue when distributing individual
information. It guarantees individual information distributing without revealing touchy
information. The much well known methodology is K-Anonymity, where information is changed
to comparability classes, each class having a set of K-records that are undefined from one
another. Yet, a few creators have called attention to various issues with K-obscurity and have
proposed procedures to counter them or stay away from them. l-variety and t-closeness are such
procedures to give some examples. Our examination has shown that this load of procedures
increment computational work to for all intents and purposes infeasible levels, however they
increment security. A couple of procedures represent a lot of data misfortune, while
accomplishing security. In this paper, we propose a novel, comprehensive methodology for
accomplishing most extreme protection with no data misfortune and least overheads (as it were
the important tuples are changed). We address the information security issue utilizing fluffy set
methodology, an aggregate outlook change and another viewpoint of taking a gander at
protection issue in information distributing. Our basically possible strategy furthermore, permits
customized protection safeguarding, and is valuable for both mathematical and all out ascribes.
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