A Privacy‐Preserving Framework for Real‐Time P2P Virtual Scene Transmission Using Homomorphic Encryption and Edge Offloading
Year: 2025
Type: article
Abstract: ABSTRACT In order to address the data privacy issues in real‐time collaboration in P2P virtual scenes, this paper proposes a privacy protection transmission framework based on the Brakerski Fan Vercauteren homomorphic encryption scheme. The proposed framework allows for direct computation of ciphertext, ensuring end‐to‐end privacy protection while maintaining acceptable real‐time performance. We have built a high‐performance experimental platform consisting of an Intel Xeon Gold edge server with 128 GB of memory, an NVIDIA RTX A6000 GPU, and a Docker based Ubuntu environment that integrates TensorFlow and PyTorch for encryption performance testing and behavioral modeling. The experimental results show that homomorphic encryption supports addition and multiplication operations in ciphertext form, effectively preventing sensitive information leakage. Compared with AES, the BFV based scheme shows higher average latency, but reduces privacy leakage by over 93%. In four real‐world collaboration scenarios, the leakage rate decreased from 86.25% to 5.5%. When the number of nodes increased from 10 to 200, the system throughput decreased from 43.2 to 28.4 MB/s, while the CPU utilization increased from 68% to 87%, indicating a reasonable trade‐off between computation and security. In addition, the integrated edge offloading mechanism reduces encryption latency from 180 to 95 ms, an increase of 36%, while maintaining complete ciphertext confidentiality during processing. These findings validate that the proposed BFV based P2P collaboration framework achieves advanced privacy protection and real‐time scalability, providing a feasible and secure solution for remote design, distributed virtual training, and other privacy sensitive applications. Future research will focus on optimizing lightweight encryption and adaptive scheduling mechanisms for wider deployment in heterogeneous P2P environments.
Source: Security and Privacy
DOI: 10.1002/spy2.70137
Author: Wangqin Liu
Institution: Nanchang Normal University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4416340818