A Novel Dynamic Multi-Layer Secret Sharing Approach for Privacy-Preserving Social Network

Maintaining privacy while maintaining a balance between anonymity and data utility is an ongoing challenge in social network data sharing. Traditional anonymisation techniques frequently violate the integrity of data structures or fall short in offering strong defence against changing hostile threats. This paper presents a unique architecture that combines Secret Sharing-Based Graph Anonymisation (SSGA) and Dynamic Layer Rotation (DLR) to address these issues. Our method creates organised unpredictability by utilising cryptographic concepts and chaotic transformations, making de-anonymization computationally challenging while maintaining the analytical usefulness of network architectures. Through empirical testing on popular datasets such as the Facebook Social Graph, Enron Email, and Karate Club datasets, we validate our approach and show that it is scalable and successful in reducing the dangers associated with de-anonymization.