Homomorphic Encryption-Based Privacy Protection Data Processing Strategies in Fog Computing
Shiyang Song, Jinhai Tang, Haozhe Wang, Dongxu Yuan, Zhiyuan Zhang · 2024
With the development of Intelligent Transportation Systems (ITS), real-time processing and privacy protection for traffic data become particularly important. In this research, we explore how to process traffic data efficiently and securely in fog computing environments, especially in wireless in-vehicle networks. First, we propose a homomorphic encryption-based strategy that performs computation on ciphertexts, eliminating the need for frequent decryption and thus achieving higher data processing efficiency. Then, we provide an in-depth discussion on data integrity and real-time performance, and propose a novel data validation method to ensure data integrity and real-time updates in fog computing environments. In addition, we designed a series of simulation experiments using virtual data to verify the effectiveness of our approach. The experimental results show that our strategy not only greatly improves the data processing speed, but also effectively guarantees the privacy and integrity of the data. Overall, this study provides a new and efficient method for traffic data processing in fog computing environments, which is an important reference value for future traffic data processing.