Data Integrity

Definition

Using hashing and checksums e.g. to ensure integrity and detect unauthorized modifications

How it works

Data integrity ensures the accuracy and consistency of data throughout its lifecycle. Techniques such as checksums, hashing, and error-checking codes generate unique identifiers for data. When the data is accessed or transmitted, these identifiers are recalculated and compared to the original values. If discrepancies are detected, it indicates potential data corruption or tampering. Redundancy, error detection, and correction mechanisms, along with secure storage practices, contribute to maintaining data integrity, crucial for reliable information storage, retrieval, and transmission while safeguarding against unauthorized alterations or corruption.

In robotics

In robotics, data integrity is paramount as it directly influences the reliability and accuracy of decision-making processes. Ensuring the integrity of data involves implementing measures to prevent, detect, and correct errors or unauthorized alterations in information collected and processed by robotic systems. From sensor readings to command inputs, maintaining data integrity is critical for accurate perception, precise control, and safe operation. Robotic systems employ error-checking mechanisms, checksums, and cryptographic techniques to verify the accuracy and authenticity of data, reducing the risk of corrupted or manipulated information. A high level of data integrity enhances the trustworthiness of robotic operations, contributing to the overall safety, efficiency, and dependability of these intelligent machines in various applications.

Considerations

Related ATTACK Techniques

References

A Study on Networked Industrial Robots in Smart Manufacturing: Vulnerabilities, Data Integrity Attacks and Countermeasures

Robotic Demands on On-Board Data Processing

OdditDB: Auditable and Adaptable Database System for Enhancing Robotics Data Integrity