Privacy diffusion in online social media reconstruction, modelling and blocking
- Publication Type:
- Thesis
- Issue Date:
- 2023
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Social media has become a ubiquitous tool for spreading news and messages. It enables communication between individuals, and is a convenient platform for people to connect, interact, communicate or share information with others on the Internet. However, as users share their personal information, privacy information can also be revealed, which can spread through the network, making it crucial to study how private information propagates across social media. Many studies have used information diffusion models to examine how information flows through social networks. However, these models are theoretical and may not behave in the same way as private information. This raises questions about the observed phenomena and differences between privacy information and normal news diffusion. To tackle these challenges, we identified four major research problems: 1) Delineating private information from the clutter of other information on social media, 2) Identifying the propagation paths of private information, 3) Determining the features of the information or the diffusion process that inform adequate protection mechanisms and 4) Modeling private information diffusion through online social media.
To address these issues, we collected spreading information on online social media to construct graph structures that show the propagation path of different information. We found that privacy information differs in propagation features and the size of star structures. We proposed a new information diffusion model that considers the probability of users receiving, forwarding, and holding interest in a message. Lastly, we designed two block mechanisms to congest the diffusion of privacy information in social media. The main contributions of this thesis are a new information diffusion model, insights into privacy information propagation features, and block mechanisms to protect privacy information in social media. The main innovations and contributions of this thesis are as follows: 1)We introduce a new model that simulates the auction process with preserving users' privacy in online social network. 2) We use the data from Twitter API to construct graph structures that depict the propagation path of different types of information in social media. 3) We discuss the problem of modeling privacy information propagation, together with the propagation features and parameters of privacy information. 4)We propose a novel mechanism to stop privacy diffusion in online social media and prevent the privacy of social media users from leakage.
5) We carry out a new privacy diffusion-blocking methods. We try to block privacy data diffusion by limiting the connection between users in online social networks.
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