AI Data Collaboration Platform With Verifiable Private Aggregation
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Solution Overview
Problem
The rapid growth of AI and machine learning has led to an exponential increase in the demand for high-quality training data, which is strained by the limited supply of quality public data online and issues of opacity, unfair compensation, and lack of control for data providers, potentially slowing AI development.
Innovation Solution
A privacy-preserving data collaboration platform utilizing adversarial question generation techniques and cryptographic shares to create benchmark tasks, ensuring transparent, auditable, and incentivized data collection, while maintaining participant privacy and data integrity through verifiable distributed aggregation functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If centralized data collection is used to train AI models, then the quantity of training data increases, but data privacy and security are compromised
Solution Approach 1:
The patent segments the centralized data collection process into distributed contributions from multiple participant nodes. Each node contributes data locally without centralizing the raw data, thus increasing training data quantity while preserving privacy through cryptographic protection at each node.
Solution Approach 2:
The patent introduces cryptographic protocols and verifiable computation mechanisms as intermediaries between data contributors and AI model trainers. These intermediaries enable data utility extraction without exposing raw data, resolving the contradiction between data quantity and privacy protection.
2Productivity
If traditional data aggregation methods are used, then data processing speed increases, but trust requirements and security vulnerabilities increase
Solution Approach 1:
The patent implements verifiable computation feedback mechanisms where each aggregation step produces provably correct results that can be independently verified. This feedback loop enables fast parallel processing while reducing trust requirements through cryptographic proof of correctness.
Solution Approach 2:
The patent replaces traditional mechanical trust-based aggregation systems with cryptographic verification mechanisms. Instead of relying on trusted intermediaries to ensure data integrity, the system uses mathematical proofs to verify aggregation correctness, enabling faster processing with reduced trust requirements.
3Adaptability or versatility
If more participant nodes are added to the collaboration platform, then data diversity and model performance improve, but system complexity and coordination overhead increase
Solution Approach 1:
The patent designs a universal protocol framework that handles diverse data types and participant nodes through standardized interfaces. This multi-functional architecture enables increased data diversity while managing system complexity through unified coordination mechanisms that work across different node types and data formats.
Data Source
AI summary
The present embodiment discloses a novel process and system for storing versioned data on the Bitcoin blockchain network. This process involves generating derived public keys linked to specific versions and data parts using a key tweaking method, and storing each data part on the blockchain using its associated derived public key. Information about versions and parts are stored in root transactions. An API is provided for managing the stored data, with options for encryption, checksums, data anchoring, timestamping, segmentation, and digital signatures. AIDIOS, a data protocol designed for direct storage and retrieval on the Bitcoin blockchain, is also included. This system allows for significant amounts of data to be stored directly on the blockchain.