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SecVerGSSL: Proof-Based Verified Computation for Graph Self-Supervised Learning
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AI
DOI：10.1109/TMC.2024.3437388.png)
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Abstract
En 中文
As graph data becomes increasingly prevalent in mobile computing scenarios, deploying Graph Convolutional Network-based self-supervised learning (GCN-SSL) models on mobile devices provides a powerful solution for analyzing graph data and enhancing the intelligence of various mobile services. However, ensuring the legitimacy and security of these models is crucial to protect against compromised or unauthorized versions that could lead to security vulnerabilities or intellectual property issues. In this work, we propose PoGSSL, a verifiable proof of GCN-SSL model training that authenticates model integrity and provenance by checking the reproducibility of the specific model training process. We then introduce SecVerGSSL from PoGSSL to provide privacy-preserving verified computation services. SecVerGSSL offloads the entire computation to the cloud and equips servers with customized secure components, enabling effective verified computation over secret-sharing encrypted training data and PoGSSL. Extensive experiments demonstrate that SecVerGSSL offers verification accuracy indistinguishable from plaintext results, with overhead on the verifier-side requiring at most 10.35 milliseconds and 65.98 KB per epoch.
Keywords:
Computational modeling
Data models
Training
Analytical models
Cryptography
Servers
Mobile computing
Graph analytics
graph convolutional networks
proof of training
self-supervised learning
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