Distributed Registers for Aeronautical Data Lifecycle
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Solution Overview
Problem
Current technologies fail to effectively manage the life cycle of aeronautical data, particularly in terms of obsolescence and privacy, due to the lack of suitable solutions for distributed data management in the aeronautical industry, with existing blockchain applications being primarily focused on financial applications and not addressing specific aeronautical needs.
Innovation Solution
The implementation of systems and methods using blockchain technology, smart contracts, and machine learning to manage the life cycle of aeronautical data, including encryption, decryption, and data quality control, with features such as time-dependent quality indicators and editable blockchain chains, to ensure secure and efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored indefinitely in centralized databases, then data availability is maintained, but data security and privacy are compromised due to concentration risks and common modes of weakness
Solution Approach 1:
The patent segments data storage across multiple distributed nodes in a blockchain network rather than concentrating it in a single database. Each node holds a copy of the distributed ledger, eliminating single points of failure while preventing centralized security risks. This segmentation maintains data availability through redundancy while addressing security concerns by removing the concentration vulnerability.
Solution Approach 2:
The patent introduces cryptographic intermediaries (public-private key pairs, hash functions, and digital signatures) that mediate data access and validation in the distributed system. These cryptographic mechanisms enable secure data transmission and access control without requiring centralized trust, thus maintaining availability while mitigating security risks associated with centralized storage.
2Reliability
If blockchain technology is used to ensure data immutability and security, then data security is improved, but data lifecycle management becomes difficult due to the immutable nature of blockchain
Solution Approach 1:
The patent extracts the immutability property from the entire data structure and applies it selectively only to critical integrity elements (hash values, timestamps, and validation records) in the blockchain. The actual aeronautical data can be updated or deleted according to lifecycle requirements, while the blockchain maintains an immutable audit trail of all changes. This selective application of immutability preserves security for critical metadata while enabling flexibility for data management.
Solution Approach 2:
The patent implements a dynamic data lifecycle management system where data transitions through different states (active, archived, deleted) with corresponding blockchain record updates. The system dynamically adjusts data retention policies and automatically triggers blockchain transactions to record state changes, maintaining security while adapting to varying data lifecycle requirements through automated smart contracts.
3Quantity of substance
If multiple actors manage aeronautical data, then data coverage and completeness are improved, but data quality control and obsolescence management become complex
Solution Approach 1:
The patent implements feedback mechanisms through automated validation rules and quality checks that operate on data upon entry into the blockchain. Each data submission is validated against predefined quality criteria, and the system provides feedback to data submitters about compliance issues. This automated feedback loop maintains high data quality standards across multiple actors without requiring complex manual coordination, enabling broad data coverage while ensuring consistent quality control.
Data Source
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AI summary
This document describes computer-implemented systems and processes for managing the lifecycle of aeronautical data stored in a blockchain. It covers the steps of receiving or transmitting aeronautical data, and encrypting and/or decrypting this data via a smart contract. The use of multiple blockchains is described, along with the facts and rules governing data lifecycle management (e.g., planned obsolescence, time-dependent quality indicators, etc.). Developments include transactional aspects; the use of oracle services; asymmetric, homomorphic, and post-quantum encryption methods; the use of chameleon hash functions to manipulate at least partially editable blockchains; and machine learning techniques. Software and system aspects are also described.