Blockchain NFT Authentication for AI Training Data Integrity
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Solution Overview
Problem
AI data poisoning attacks compromise the integrity and reliability of machine learning models by manipulating training data, which is difficult to detect and can persist and adapt, posing a threat to various applications and scenarios.
Innovation Solution
A blockchain-based system generates non-fungible tokens (NFTs) for identified data fields within training data records, storing them on a blockchain to ensure data authenticity and integrity, thereby preventing unauthorized alterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data validation methods are used for training data, then the system is simpler and faster, but the data can be easily manipulated and poisoned without detection
Solution Approach 1:
The patent introduces a blockchain as an intermediary system between data sources and machine learning models. The blockchain provides a decentralized, immutable ledger that records data provenance and authentication information, enabling verification of data authenticity without requiring complex centralized validation systems. This intermediary layer simplifies the overall system architecture while significantly improving data reliability.
Solution Approach 2:
The patent implements preliminary authentication actions by validating and recording data information on the blockchain before the data is used for training machine learning models. This preliminary action ensures that only authenticated, unmanipulated data enters the training pipeline, preventing poisoning attacks before they can occur. The authentication process includes generating cryptographic hashes and recording them on the blockchain in advance.
2Reliability
If comprehensive data authentication is implemented, then data security and integrity are improved, but computational resources are consumed and processing time increases
Solution Approach 1:
The patent extracts only the essential authentication information from the comprehensive data validation process. Instead of validating every aspect of the data in detail, the system extracts key metadata (such as data source, creation timestamp, cryptographic hash) and records this minimal information on the blockchain. This extraction approach maintains data integrity verification while significantly reducing computational overhead and processing time compared to comprehensive validation.
Solution Approach 2:
The patent creates a cryptographic copy (hash) of the data that can be verified without processing the actual data content. By storing only the hash value and metadata on the blockchain rather than the full data itself, the system enables authentication verification through simple cryptographic comparison operations. This copying approach allows for efficient verification of data integrity without consuming computational resources to process or store the entire data dataset.
Data Source
AI summary
The disclosed system includes a memory and a processor designed to execute operations for generating non-fungible tokens for training data utilized in training machine learning models. The memory is configured to store both data records of the training data and their corresponding metadata. The processor performs operations to identify a set of fields within each data record and to annotate each field. Such annotation process involves creating field metadata, including information that identifies each field in the data record and assigning a label to each field. Additionally, for each field, the processor is further configured to generate a non-fungible token and create a non-fungible token attribute record, incorporating details from both the data record metadata and the field metadata. Also, the processor is configured to store the non-fungible token attribute record in the memory, thereby facilitating comprehensive and secure authentication of data records.