Enhanced Privacy Policy Based Friend-To-Friend Content Dissemination System

Abstract

When users are geographically clustered into communities. Despite its promise, none of the proposed systems have not been widely adopted due to unbounded high content delivery security and privacy concerns. This paper, presents a privacy concerns of users,  The system exploit the fact that users will trust their friends, and by replicating content on friends’ devices who are likely to consume that content it will be possible to disseminate it to other friends when connected to low cost networks. The paper provides a formal definition of this content replication problem, Then, it presents a community based greedy heuristic algorithm with novel dynamic centrality metrics that replicates the content on a minimum number of friends’ devices, to maximize availability. Then using real world and synthetic datasets, the effectiveness of the proposed scheme is demonstrated.

In the existing system, probability of using users’ encounters is highly dependent on their social behavior. Hence, there is a diurnal correlation of opportunistic encounters among users. These patterns have been extensively analyzed. Usually, social behavior of the majority of users has weekly routines. In existing, there is higher probability that a user meets the same people at the same day and time in every week. This predictive regularity of encounter patterns can be leveraged of efficient content replication. And to evaluate the content delivery success rate use Greedy helper selection and compare the delivery performance of the proposed system against the cases where No replication is performed and Random replication, which is the simplest way of selecting helpers without any knowledge about the contact patterns among consumers.

The proposed system is providing the privacy policies of each category of images/contents are analyzed for the policy prediction.