Homomorphic Encryption Enabled SVM for Preserving Privacy of P2P Communication

In recent years, the protection of our data privacy has become crucial, particularly within the realm of peer-to-peer (P2P) communication networks. we propose a novel homomorphic encryption framework a deep dive into the exploration of cutting-edge methods to ensure the preservation of privacy, placing special emphasis on the effective utilization of homomorphic encryption. Our approach strives to uphold the confidentiality of encrypted data while maintaining functionality, achieved through the integration of defensive encryption, a machine-learning classifier algorithm, and homomorphic encryption. We conduct a thorough comparative analysis both before and after incorporating homomorphic encryption. This entails the implementation of sophisticated encryption algorithms on the Support Vector Machine (SVM) model, covering diverse aspects such as data sources, transmission routes, and individual devices, using the p2p communication threats dataset. The study illuminates the inherent link between strengthening endpoint security and the adoption of homomorphic data encryption, underscoring the significance of network-level privacy controls in the realm of peer-to-peer communication. This research elevates the overall privacy architecture by harnessing Support Vector Machines (SVM) for anomaly detection and predictive analytics. In doing so, it clarifies the fundamental relationship between enhancing endpoint security and embracing homomorphic data encryption. Essentially, this work introduces novel perspectives on reinforcing privacy security within decentralized communication networks.