Decentralized AI Access Control With Encrypted Vaults and Ledger Traceability
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Solution Overview
Problem
Existing technologies lack a decentralized system that can securely link and synthesize AI information from multiple independent parties while ensuring data ownership and control, enabling collaborative AI development and operation while maintaining privacy and security.
Innovation Solution
A decentralized database system using encrypted data vaults and an electronic decentralized ledger to store and manage AI information, allowing controlled access and traceability, with each party maintaining encryption keys and access permissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI information is stored in decentralized databases with encryption, then data security and privacy are improved, but data accessibility and collaboration capability deteriorate
Solution Approach 1:
The system segments data into two categories: sensitive AI information (models, training data, annotations) stored in encrypted decentralized databases, and non-sensitive metadata (descriptions, hashes, access permissions) stored in searchable indexes. This segmentation allows encrypted data to remain secure while metadata enables efficient discovery and access control without compromising security.
Solution Approach 2:
The patent introduces an intermediary access control mechanism that uses public-key cryptography and smart contracts. The providing computer generates cryptographic key pairs, shares public keys with requesting computers, and uses smart contracts to automate access permission verification. This intermediary layer enables secure access to encrypted data without requiring centralized control or decryption of the actual AI information.
2Adaptability or versatility
If AI information is shared across multiple parties, then collaborative AI development capability is improved, but data ownership control and traceability deteriorate
Solution Approach 1:
The system implements feedback through an immutable ledger that automatically records all access events, operations, and data usage. Each time a requesting computer accesses or operates on AI information, the smart contract logs the event with timestamps, participant identities, and operation details. This automatic feedback mechanism provides continuous traceability of data ownership and usage without hindering collaborative operations.
Solution Approach 2:
The patent applies preliminary action by establishing smart contracts and access control policies before any data sharing occurs. The providing computer pre-configures permission sets, defines operation constraints, and sets up automated verification mechanisms. These preliminary arrangements enable seamless collaboration while maintaining ownership control, as the rules are enforced automatically before any actual data access or modification takes place.
3Ease of operation
If centralized solutions are used to manage AI information, then access control simplicity is improved, but data sovereignty and decentralization benefits deteriorate
Solution Approach 1:
The system implements self-service through automated smart contracts that handle access control without requiring centralized intervention. The providing computer autonomously generates cryptographic keys, configures permission sets, and deploys smart contracts that automatically verify and enforce access policies. This self-service approach maintains data sovereignty while achieving access control simplicity through automation rather than centralized management.
Solution Approach 2:
The patent creates a universal access control framework based on standardized permission sets and operation types that can be applied across different AI information types and collaborating parties. The smart contract system provides multi-functional capabilities: it handles authentication, authorization, access control, operation verification, and logging through a single decentralized protocol. This universal mechanism serves multiple purposes while maintaining data sovereignty without requiring party-specific centralized solutions.
Data Source
AI summary
Decentralized database technology is applied to decentralized artificial intelligence (AI) information enabling collaborative AI techniques to enhance the performance of AI models and gain new insights with larger and/or better curated data sets. AI information from multiple independent sources is linked together and synthesized to render the AI information searchable, discoverable, accessible, and traceable, while also ensuring that information remains under its source's control over its lifetime, e.g., controlling the conditions under which other entities may access which pieces of AI information at a particular time.