On the Design and Implementation of the External Data Integrity Tracking and Verification System for Stream Computing...

Area of Science:

• Computer Science
• Information Security
• Data Science

Background:

• Data integrity is crucial for IoT big data security and availability.
• Stream computing systems in IoT face challenges in verifying data integrity due to real-time, volatile, and disordered data.
• Existing solutions for stream data integrity verification are not mature or universally applicable.

Purpose of the Study:

• To develop a robust data integrity verification algorithm for stream computing systems in IoT environments.
• To construct an external system for real-time tracking and analysis of message data streams.
• To enable timely detection of data corruption or loss, and implement active error alarming and message recovery.

Main Methods:

• Utilized homomorphic message authentication code and pseudo-random function security assumptions to construct the Stream-based Data Integrity Verification (S-DIV) algorithm.
• Developed an external system based on S-DIV for real-time tracking and verification of data integrity throughout the message lifecycle.
• Conducted formal security analysis under the standard model and implemented the S-DIV scheme in a simulation environment.

Main Results:

• The S-DIV scheme effectively guarantees data integrity for IoT stream data.
• The system can detect data corruption or loss in real time.
• Experimental results demonstrate that the scheme ensures data integrity within an acceptable time frame without compromising the original system's efficiency.

Conclusions:

• The proposed S-DIV scheme offers a viable solution for verifying data integrity in IoT stream computing.
• The integrated tracking and verification system enhances the reliability and security of IoT data streams.
• The approach provides timely error detection and recovery mechanisms, crucial for real-time IoT applications.