Xingliang Yuan

Securely Outsourcing Neural Network Inference to the Cloud With Lightweight Techniques

2022 · IEEE Transactions on Dependable and Secure Computing

Neural network (NN) inference services enrich many applications, like image classification, object recognition, facial verification, and more. These NN inference services are increasingly becoming an essential offering from cloud computing providers, where end-users’ data are …

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

2022 · IEEE Transactions on Dependable and Secure Computing

Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only …

Model Extraction Attacks on Graph Neural Networks

2022 · Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security

Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker pretending as a client. Unfortunately, …

Penny Wise and Pound Foolish: Quantifying the Risk of Unlimited Approval of ERC20 Tokens on Ethereum

2022

The prosperity of decentralized finance motivates many investors to profit via trading their crypto assets on decentralized applications (DApps for short) of the Ethereum ecosystem. Apart from Ether (the native cryptocurrency of Ethereum), many ERC20 …

Projective Ranking-based GNN Evasion Attacks

2022 · IEEE Transactions on Knowledge and Data Engineering

Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial attacks. Two primary limitations of the current evasion attack methods are highlighted: (1) The currentGradArgmaxignores the “long-term” …