ADAPTIVE DIFFUSION OF SENSITIVE INFORMATION IN ONLINE PUBLIC NETWORKS

The Pouncing of Delicate Information like Private Data or Rumours is a serious issue in Online social media Now-a-days. One Solution for stopping the Pouncing of Delicate Data is Limiting the spreading among social media users. However, the spreading of Limiting measures also stops the spreading of Non-Delicate Data which will result in worst experience for the user. To handle this situation, in our paper, we will study the problem of how to reduce the Delicate data while storing the non-Delicate data as same without effecting it. In our study we use known Diffusion credentials of all users for Completely-Known Networks and unknown Diffusion credentials of some users for Partially-Known Networks in Prior. For this we use Bandit Framework to Combinedly design the Result with Polynomial Convolution in both situations. Finally, we can say that our solution ensures that non-Delicate Diffusion loss will be 40% less compared to four baseline Algorithm.