IPFS-Based AI Data Retrieval for Scalable Personalized Processing
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Solution Overview
Problem
Centralized cloud servers face scalability issues, system downtimes, data security risks, and increased costs when handling large volumes of data for AI models, leading to potential financial losses and data leakage.
Innovation Solution
A decentralized distributed file storage system (IPFS) combined with a generative AI model, where personalized data is fragmented and stored across interconnected IPFS nodes, utilizing CID information for data integrity and proximity-based data retrieval, enabling secure, efficient, and scalable data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored on centralized cloud servers, then data access and processing can be performed, but scalability deteriorates when the number of users increases or data volume surges
Solution Approach 1:
The patent divides the centralized storage system into multiple distributed IPFS nodes, each storing fragments of data. This segmentation allows the system to scale horizontally by adding more nodes without affecting the performance of existing nodes, resolving the scalability limitation of centralized systems.
Solution Approach 2:
The patent introduces a new dimension of distribution by implementing data storage across multiple geographic locations and network layers. The IPFS network operates as a decentralized peer-to-peer system, adding spatial and architectural dimensions that enable parallel data access and improve overall system capacity.
2Productivity
If data is concentrated on centralized cloud servers, then AI models can process data efficiently, but system reliability deteriorates during power outages or system failures
Solution Approach 1:
The patent implements local data caching on IPFS nodes, where each node stores data fragments locally. This allows AI models to access data from nearby nodes rather than relying on a single centralized server, ensuring continued operation during local failures and improving overall system reliability.
Solution Approach 2:
The patent employs redundant data storage across multiple IPFS nodes before failures occur. Each data fragment is replicated across several nodes, creating a cushion against failures. When a node fails, the system can retrieve data from other nodes, preventing complete system downtime.
3Quantity of substance
If massive volumes of data are concentrated on centralized cloud servers, then AI models can learn from large-scale data, but data security deteriorates due to increased hacking or data leakage risk
Solution Approach 1:
The patent fragments data into multiple pieces and distributes them across numerous IPFS nodes. This segmentation means that even if one node is compromised, the complete data set remains secure. An attacker would need to compromise multiple nodes simultaneously to obtain meaningful data, significantly increasing the security threshold.
Solution Approach 2:
The patent extracts data from the centralized server environment and places it in a decentralized IPFS network. This extraction removes the single point of failure and concentration risk, distributing data security responsibilities across multiple independent nodes and reducing the overall attack surface.
4Reliability
If data is stored on centralized cloud servers, then system operation can be maintained, but operational costs increase due to the need for robust centralized infrastructure
Solution Approach 1:
The patent implements a peer-to-peer architecture where IPFS nodes serve each other directly without requiring a centralized coordination server. Each node independently manages its own data storage and retrieval operations, eliminating the need for expensive centralized infrastructure and reducing operational costs.
Solution Approach 2:
The patent creates a multi-functional IPFS node system where each node can simultaneously store data, retrieve data, and participate in network consensus. This universal functionality eliminates the need for specialized hardware or software components, reducing infrastructure costs while maintaining system reliability.
Data Source
AI summary
The present invention discloses a data processing method and system operating in an environment where a decentralized distributed file storage system (InterPlanetary File System; IPFS) is applied. The data processing method includes a step of dividing each of at least one personalized data and distributing and storing them across IPFS nodes that are interconnected and synchronized via a network; a step of receiving query information of a processing request for data generation referencing the at least one personalized data through a generative artificial intelligence model; and a step of referencing the at least one personalized data from an IPFS node that is physically adjacent to a processing server operating the generative artificial intelligence model among the IPFS nodes, and generating response data corresponding to the query information.