Modeling the Impact of Privacy on Information Diffusion in Social Networks

Humans like to disseminate ideas and news, as proved by the huge success of online social networking platforms such as Facebook or Twitter. On the other hand, these platforms have emphasized the dark side of information spreading, such as the diffusion of private facts and rumors in the society. Fortunately, in some cases, online social network users can set a level of privacy and decide to whom to show their information. However, they cannot control how their friends will use this information. The behavior of each user depends on her attitude toward privacy, that has a crucial role in the way information propagates across the network. With the aim of providing a mathematical tool for measuring the exposure of networks to privacy leakage risks, we extend the classic Susceptible-Infectious-Recovered (SIR) epidemic model in order to take the privacy attitude of users into account. We leverage such model to measure the contribution of the privacy attitude of each individual to the robustness of the whole network to the spread of personal information, depending on its structure and degree distribution. We study experimentally our model by means of stochastic simulations on four synthetic networks generated with classical algorithms.