Blockchain-Based Stochastic Gradient Descent Verification
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Solution Overview
Problem
Current centralized databases face challenges in securely and efficiently verifying stochastic gradient descent (SGD) updates in machine learning models without revealing private data, leading to potential data compromise and lack of trust among participants.
Innovation Solution
Implementing a blockchain-based system that allows for decentralized storage and verification of SGD updates through homomorphic encryption, enabling private endorsement and consensus mechanisms without sharing raw data, ensuring data privacy and trust among participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized database stores private data in a single location for easy management and control, then ease of operation and security control are improved, but data privacy and trust among participants deteriorate due to potential data compromise and lack of verification
Solution Approach 1:
The patent segments the centralized database into multiple decentralized nodes distributed across a blockchain network. Each node maintains a copy of the ledger, eliminating the single point of control while improving both operational ease through automated consensus mechanisms and reliability through distributed verification of SGD updates.
Solution Approach 2:
The patent introduces homomorphic encryption as an intermediary layer that enables verification of SGD updates without revealing private data. This cryptographic mediator allows participants to verify model training integrity while maintaining data privacy, resolving the trust issue in centralized systems.
2Loss of substance
If centralized database minimizes data redundancy through single storing place, then data redundancy is reduced, but verification capability and trust among participants deteriorate
Solution Approach 1:
The patent segments the single centralized storage into multiple distributed nodes, each holding a copy of the blockchain ledger. This segmentation creates controlled redundancy that actually improves verification capability, as multiple nodes can independently verify SGD updates, while maintaining efficient data utilization through the immutable ledger structure.
3Reliability
If blockchain system implements decentralized storage and homomorphic encryption for SGD verification, then data privacy and trust are improved, but device complexity and computational requirements worsen
Solution Approach 1:
The patent implements self-service mechanisms where the blockchain network automatically performs verification of SGD updates through consensus protocols, and homomorphic encryption automatically enables privacy-preserving computations. This reduces the need for complex manual verification processes and centralized trust management, thereby managing complexity through automation.
Solution Approach 2:
The patent changes the computational parameters by implementing homomorphic encryption, which allows mathematical operations on encrypted data. This parameter change enables verification without decryption, maintaining data privacy while managing computational complexity through efficient cryptographic algorithms designed for this purpose.
Data Source
AI summary
An example operation includes one or more of computing, by a data owner node, updated gradients on a loss function based on a batch of private data and previous parameters of a machine learning model associated with a blockchain, encrypting, by the data owner node, update information, recording, by the data owner, the encrypted update information as a new transaction on the blockchain, and providing the update information for an audit.