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, how ever 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.