Guowen Xu

Secure and Verifiable Inference in Deep Neural Networks

2020 · Annual Computer Security Applications Conference

Outsourced inference service has enormously promoted the popularity of deep learning, and helped users to customize a range of personalized applications. However, it also entails a variety of security and privacy issues brought by untrusted …

SIMC 2.0: Improved Secure ML Inference Against Malicious Clients

2022 · arXiv (Cornell University)

In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem …

Incremental Learning, Incremental Backdoor Threats

2022 · IEEE Transactions on Dependable and Secure Computing

Class incremental learning from a pre-trained DNN model is gaining lots of popularity. Unfortunately, the pre-trained model also introduces a new attack vector, which enables an adversary to inject a backdoor into it and further …

PriFR: Privacy-preserving Large-scale File Retrieval System via Blockchain for Encrypted Cloud Data

2023

As a fundamental and commonly used service, file retrieval has been extensively studied by information retrieval, cryptography, and big data communities. In this paper, we consider the problem of privacy-preserving file retrieval. A new framework …

Verifiable and Lightweight Multi-Round Secure Federated Learning

2025 · IEEE Transactions on Dependable and Secure Computing

Federated learning (FL) is a paradigm that ensures the confidentiality and accessibility of data without requiring the collection of private data from multiple sources. It acquires an aggregation model by integrating various local models from …