Chameleon Hash based Collaborative Time-Series Data Integrity Monitoring
Yi Li, Jian Shen, Mohammad S. Obaidat, Pandi Vijayakumar, Selvaraju Sendhilkumar, Kuei‐Fang Hsiao · ACM Transactions on Autonomous and Adaptive Systems · 2025
The importance of the ocean to humanity is undeniable, whether in terms of ecology, climate, resources. Utilizing collected ocean data combined with AI to achieve adaptive and automated processing and prediction is a current research focus. The effectiveness of AI applications largely depends on the integrity of ocean data. Ocean data has three characteristics: vast spatial coverage, long temporal duration, and large volume. Traditional cloud-based data integrity verification methods are no longer suitable. Ocean data should be processed on edge servers located closer to the data collection points and then sent to the appropriate data storage servers. The data processing methods should be lightweight to accommodate the sequential characteristics of data. Moreover, the data integrity monitoring process should be collaboratively completed on the data storage servers without the need for a central third party. To this end, we propose a ocean data integrity monitoring protocol. It generates data for different storage servers, using sensor sampling periods and data masks, and utilizes chameleon hash with ephemeral trapdoors to generate validators, thus supporting mutual integrity monitoring among storage servers. Experiments demonstrate that our scheme compared to the latest solutions, not only meets security requirements but also offers advantages of computational overhead.