Privacy-Preserving Machine Learning Models for Network Anonymization

Abstract

With the increasing prevalence of digital communication and the continuous growth of networked systems, the need for privacy-preserving techniques in machine learning models for network anonymization has become paramount. Traditional approaches to network anonymization often compromise individual user privacy, making it imperative to develop models that strike a balance between data utility and personal information protection. This research explores privacy-preserving machine learning models tailored for network anonymization, aiming to provide robust solutions that safeguard user identities while maintaining the effectiveness of network analysis. The proposed models leverage advanced cryptographic techniques, differential privacy, and federated learning to ensure that sensitive information remains secure during the model training process. The core focus of this study is on developing algorithms that can accurately analyze network data without jeopardizing the privacy of individuals. By incorporating techniques such as homomorphic encryption and secure multi-party computation, the models presented in this research allow for the extraction of valuable insights from network datasets without revealing sensitive details about specific users.