Blockchain-based Framework for Generating and Managing Unlearnable Examples for Enhancing Data Privacy and Access Control

Summary

This paper proposes a blockchain-based framework for generating unlearnable examples, integrating dynamic perturbation with access control. It enhances privacy protection by ensuring that unauthorized users receive perturbed data, safeguarding sensitive information while enabling efficient data management and access through smart contracts.

Abstract

In the context of the rapid development of large language models (LLMs), contrastive learning has become widely adopted due to its ability to bypass costly data annotation by leveraging vast amounts of network data for model training. However, this widespread use raises significant concerns regarding data privacy protection. Unlearnable Examples (UEs), a technique that disrupts model learning by perturbing data, effectively prevents unauthorized models from misusing sensitive data. However, existing methods for generating UEs face two primary challenges: first, perturbations may be reversed using techniques such as reverse purification or denoising, including diffusion models that remove protective perturbations in image UEs; second, once data is published, ensuring data traceability and managing access control becomes difficult. To address these issues, this paper proposes a Blockchain-Integrated Unlearnable Example Generation and Management Framework (B-UEGMF) for generating and managing UEs. By leveraging the decentralized and immutable properties of blockchain, we store example hash values on the blockchain and dynamically manage data access rights through smart contracts. Additionally, UEs are generated using a multi-objective perturbation technique, Dynamic Error-Minimizing Noise (DEM), which enhances robustness against reversal methods. We also provide a quantitative evaluation of the privacy protection capabilities of the generated examples. Experimental results demonstrate that the proposed framework significantly improved the defense of UEs against reverse attacks while ensuring efficient data privacy management.

Introduction

In recent years, with the rapid advancement of deep learning and large language models, contrastive learning has emerged as an efficient unsupervised learning approach due to its independence from costly manual annotations^1,2. However, the extensive use of public datasets has raised significant concerns about privacy breaches and data misuse. Instances of unauthorized utilization of publicly available data for model training have become increasingly common³. For example, in 2017, unauthorized public photographs were employed to train facial recognition models⁴. S....

Protocol

Setup
We considered a supervised classification task with a dataset , where represents the input features and

Results

Blockchain and smart contract framework
Experimental results demonstrated that the proposed Blockchain-Integrated Unlearnable Example Generation and Management Framework (B-UEGMF), combined with smart contracts, enabled effective dynamic management of client-specific access to data. For authorized users, the retrieved clean data achieved a test accuracy of 90.2% on a ResNet-18 surrogate model evaluated on the CIFAR-10 dataset. In contrast, unauthorized users accessing UEs generated by DEM obtained a .......

Discussion

The integration of blockchain and UEs has advanced the field of data privacy protection by providing a transparent and decentralized solution for managing data access. Unlike conventional privacy-preserving methods, which often rely solely on perturbation techniques³¹, this study bridges the gap between data protection and responsibility tracing. In federated learning scenarios, the proposed framework ensures secure and private model training across decentralized datasets, mitigating the risk of u.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CUDA 12.1	NVIDIA		Used to enhance the performance of deep learning applications
NVIDIA A800 80GB PCIe A800 80GB PCIe	NVIDIA		Used for deep learning model training
Python 3.10	Python Software Foundation		Used for data preprocessing and analysis
PyTorch 2.5.1	Facebook		Deep learning framework used for model training
Ubuntu 22.04	Canonical		Operating system used for setting up the environment