A Study on Privacy Preserving in Big Data Mining Using Fuzzy Logic Approach (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
Information security is the most acclaimed issue when distributing individualinformation. It guarantees individual information distributing without revealing touchyinformation. The much well known methodology is K-Anonymity, where information is changedto comparability classes, each class having a set of K-records that are undefined from oneanother. Yet, a few creators have called attention to various issues with K-obscurity and haveproposed procedures to counter them or stay away from them. l-variety and t-closeness are suchprocedures to give some examples. Our examination has shown that this load of proceduresincrement computational work to for all intents and purposes infeasible levels, however theyincrement security. A couple of procedures represent a lot of data misfortune, whileaccomplishing security. In this paper, we propose a novel, comprehensive methodology foraccomplishing most extreme protection with no data misfortune and least overheads (as it werethe important tuples are changed). We address the information security issue utilizing fluffy setmethodology, an aggregate outlook change and another viewpoint of taking a gander atprotection issue in information distributing. Our basically possible strategy furthermore, permitscustomized protection safeguarding, and is valuable for both mathematical and all out ascribes.