A Study on Privacy Preserving in Big Data Mining Using Fuzzy Logic Approach
Main Article Content
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.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
