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Related Experiment Video

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Achieving Verifiable Decision Tree Prediction on Hybrid Blockchains.

Moxuan Fu¹, Chuan Zhang^1,2, Chenfei Hu¹

¹School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

Entropy (Basel, Switzerland)

|July 29, 2023

Summary

This study introduces VDT, a novel scheme for verifiable decision tree predictions on blockchain. It ensures the integrity of machine learning model outputs in cloud services, offering efficient verification proofs.

Keywords:

Merkle tree authenticated data structure blockchain decision tree machine learning verifiable

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Area of Science:

Computer Science
Artificial Intelligence
Cybersecurity

Background:

Machine learning, particularly decision trees, is widely used in cloud services.
Ensuring the integrity and correctness of online decision tree predictions is a significant challenge.
Blockchain technology offers immutable and traceable features suitable for verifiable machine learning.

Purpose of the Study:

To propose a secure and verifiable scheme for decision tree prediction services on blockchain systems.
To address the growing concern of integrity issues in online decision tree predictions.
To enable clients to verify the correctness of model predictions from service providers.

Main Methods:

Leveraging Merkle trees and hash functions to create verification proofs for decision tree predictions.
Developing a Verifiable Decision Tree (VDT) prediction scheme.
Extending the scheme with an efficient update method for verifiable decision trees.

Main Results:

The proposed VDT scheme enables service providers to generate verification proofs for decision tree predictions.
The scheme allows for efficient updates to the verifiable decision tree model.
Security proofs demonstrate the robustness of the VDT schemes.
Experimental evaluations show that proof generation takes less than one second.

Conclusions:

The VDT scheme effectively enhances the security and verifiability of decision tree predictions in cloud environments.
Blockchain integration provides a reliable framework for secure machine learning services.
The proposed methods offer a practical solution for ensuring trust in online machine learning predictions.