Unveiling the Veiled: Leveraging Deep Learning and Network Analysis for De-Anonymization in Social Networks

Abstract

Online anonymity allows individuals to safeguard their personal information. It provides the freedom for anyone to express themselves without being concerned about censorship, discrimination, or retaliation. This pseudonym also provides opportunities for individuals to promote open discourse and diverse viewpoints. However, in today's digital age, this online anonymity has become a growing concern. Although the safeguarding of people’s rights, the advancement of free speech, and the development of a more diverse and democratic online community all depend heavily on the anonymity of online users, there are risks as well, such as cyberbullying, harassment, and the propagation of false information. Because of their anonymity, predators, groomers, and other unscrupulous individuals may be able to take advantage of vulnerable individuals, especially children and adolescents. In order to trick victims into hazardous or abusive situations, adversaries can hide their identities. Deep learning and network analysis can be used to reveal the true identities of anonymous social media users in order to combat this. Deep learning algorithms are capable of analyzing a wide range of social network data, including user behavior, relationships, and content, in order to find patterns and correlations that can lead to the true identities of users that go anonymous. The proposed system includes examining network topology and group dynamics to identify potential anomalies and connections that could lead to de-anonymization. This paper proposes a novel module for de-anonymization integrating deep learning and network analysis in social networks.