New Techniques for Limiting Misinformation Propagation

This paper focuses on limiting misinformation propagation in networks. Its first contribution is introducing the notion of vaccinated observers, which is a node enriched with additional power. Vaccination is adding, locally, a plugin or asking for the help of a trusted third party, called a trusted authority. The plugin or the authority is able to detect if the received information is misinformation or not. Vaccinated Observers must stop forwarding detected misinformation. Based on this notion, two algorithms for limiting misinformation are proposed. The second contribution of the paper is an algorithm based on Moving Observers for locating a strong adversary diffusion source. This algorithm selects a random subset of nodes as observers for a random period <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>. This means that the observer subset may change over time in a randomized manner. Consequently, the strong adversary diffusion source can’t have global knowledge about observers positions. Having these positions by the diffusion source will make its localization by the observers more complicated, even impossible. The third contribution is proposing an algorithm for stopping misinformation propagation based on a punishment strategy. This algorithm has a very simple principle design and it assumes that an authority or a mechanism <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> is available. The authority <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> has the ability to detect if the received information is misinformation or not. If a node <inline-formula> <tex-math notation="LaTeX">$n_{i}$ </tex-math></inline-formula> receives information <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> from its neighbor <inline-formula> <tex-math notation="LaTeX">$n_{j}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> is detected, by <inline-formula> <tex-math notation="LaTeX">$n_{i}$ </tex-math></inline-formula> via the authority <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula>, as misinformation then <inline-formula> <tex-math notation="LaTeX">$n_{j}$ </tex-math></inline-formula> is punished for a period <inline-formula> <tex-math notation="LaTeX">$pp$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$pp$ </tex-math></inline-formula> stands for punishment period). If the node <inline-formula> <tex-math notation="LaTeX">$n_{j}$ </tex-math></inline-formula> repeats this action for <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> time then the punishment period increases to <inline-formula> <tex-math notation="LaTeX">$n*pp$ </tex-math></inline-formula>. The punishment in this algorithm is stopping the forwarding of the information received from a punished node <inline-formula> <tex-math notation="LaTeX">$n_{j}$ </tex-math></inline-formula>. The simulation results show that the proposed techniques are both efficient and accurate while locating the diffusion source. Consequently, misinformation propagation is limited.