ADAPTIVE DIFFUSION OF SENSITIVE INFORMATION IN ONLINE SOCIAL NETWORKS

Abstract

The cascading of sensitive information such as private contents and rumors is a severe issue in
online social networks. One approach for limiting the cascading of sensitive information is constraining the
diffusion among social network users. However, the diffusion constraining measures limit the diffusion of nonsensitive
information diffusion as well, resulting in the bad user experiences. To tackle this issue, in this paper, we
study the problem of how to minimize the sensitive information diffusion while preserving the diffusion of nonsensitive
information, and formulating it as a constrained minimization problem. We study the problem of interest
over the fully-known network with known diffusion abilities of all users and the semi-known network where
diffusion abilities of partial users remain unknown in advance. By modeling the sensitive information diffusion
size as the reward of a bandit, we utilize the bandit framework to jointly design the solutions with polynomial
complexity in both the scenarios. Moreover, the unknown diffusion abilities over the semi-known network induced
make it difficult to quantify the information diffusion size in algorithm design. For this issue, we propose to learn
the unknown diffusion abilities from the diffusion process in real time and then adaptively conduct the diffusion
constraining measures based on the learned diffusion abilities, relying on the bandit framework. Extensive
experiments on real and synthetic datasets demonstrate that our solutions can effectively constrain the sensitive
information diffusion, and enjoy a 40% less diffusion loss of non-sensitive information comparing with four
baseline algorithms.