A survey on various available techniques for preventing sensitive information in social network from different attacks

Abstract

On-line social networks like Facebook square measure progressively utilized by many of us. These
networks permit users to publish their own details and change them to contact their friends. A number of the data
disclosed within these networks is non-public. These networks permit users to publish details regarding themselves
and to attach to their friends. Here we have devised the possible techniques for data sanitization and concluded that
collective method is most efficient amongst them. A privacy breach happens once sensitive data regarding the user, the
data that a personal desires to stay from public, is disclosed to associate in nursing soul. Non-public data escape might
be a very important issue in some cases. And explore a way to launch reasoning attacks exploitation discharged social
networking knowledge to predict non-public data. During this we have a tendency to map this issue to a collective
classification drawback and propose a collective reasoning model. In our model, Associate in nursing assailant
utilizes user profile and social relationships in a very collective manner to predict sensitive data of connected victims
in a very discharged social network dataset. To safeguard against such attacks, we have a tendency to propose a
knowledge sanitation methodology conjointly manipulating user profile and friendly relationship relations.