Open AccessSecured Multi-Party Data Release on Cloud for Big Data Privacy-Preserving Using Fusion Learning
Author(s) -
Divya Dangi et.al
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i3.1893
Subject(s) - computer science , generalization , data publishing , data mining , cloud computing , k anonymity , information sensitivity , anonymity , computer security , publishing , mathematical analysis , mathematics , political science , law , operating system
Previous computer protection analysis focuses on current data sets that do not have an update and need one-time releases. Serial data publishing on a complex data collection has only a little bit of literature, although it is not completely considered either. They cannot be used against various backgrounds or the usefulness of the publication of serial data is weak. A new generalization hypothesis is developed on the basis of a theoretical analysis, which effectively decreases the risk of re-publication of certain sensitive attributes. The results suggest that our higher anonymity and lower hiding rates were present in our algorithm. Design and Implementation of new proposed privacy preserving technique: In this phase proposed technique is implemented for demonstrating the entire scenario of data aggregation and their privacy preserving data mining. Comparative Production between the proposed technology and the traditional technology for the application of C.45: In this stage, the performance is evaluated and a comparative comparison with the standard algorithm for the proposed data mining security model is presented