Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environments

TL;DR

Panther is a novel framework that significantly reduces the computational, communication, and financial costs of privacy-preserving GNN training and inference in cloud environments by leveraging four-party computation and neighbor padding.

Contribution

It introduces a cost-effective privacy-preserving framework for GNNs that outperforms existing methods in efficiency and cost savings, enabling wider adoption in cloud settings.

Findings

Reduces GNN training time by 75.28%

Lowers inference time by 82.80%

Saves approximately 55% in financial costs

Abstract

Graph Neural Networks (GNNs) have marked significant impact in traffic state prediction, social recommendation, knowledge-aware question answering and so on. As more and more users move towards cloud computing, it has become a critical issue to unleash the power of GNNs while protecting the privacy in cloud environments. Specifically, the training data and inference data for GNNs need to be protected from being stolen by external adversaries. Meanwhile, the financial cost of cloud computing is another primary concern for users. Therefore, although existing studies have proposed privacy-preserving techniques for GNNs in cloud environments, their additional computational and communication overhead remain relatively high, causing high financial costs that limit their widespread adoption among users. To protect GNN privacy while lowering the additional financial costs, we introduce Panther, a cost-effective privacy-preserving framework for GNN training and inference services in cloud environments. Technically, Panther leverages four-party computation to asynchronously executing the secure array access protocol, and randomly pads the neighbor information of GNN nodes. We prove that Panther can protect privacy for both training and inference of GNN models. Our evaluation shows that Panther reduces the training and inference time by an average of 75.28% and 82.80%, respectively, and communication overhead by an average of 52.61% and 50.26% compared with the state-of-the-art, which is estimated to save an average of 55.05% and 59.00% in financial costs (based on on-demand pricing model) for the GNN training and inference process on Google Cloud Platform.