题 目：Hardware-assisted Privacy-preserving Decision Tree Training and Inference
内容简介：Decision tree (DT) is a widely used machine learning model due to its versatility, speed, and interpretability. However, for privacy-sensitive applications, outsourcing DT training and inference to cloud platforms raises concerns about data privacy. Researchers have developed privacy-preserving approaches for DT training and inference using cryptographic primitives, such as Secure Multi-Party Computation (MPC). While these approaches have shown progress, they still suffer from heavy computation and communication overheads. This talk will discuss the potential of using hardware: Trusted Execution Environments (TEE) and GPUs to speed up the DR training and inference. Our study has found that hardware-assisted DT training and inference significantly outperform pure software-based solutions without any loss on security guarantee.
报 告 人：崔书杰
报告人简介：Dr. Shujie Cui is a Lecturer at Monash University in the Faculty of Information Technology. She obtained her PhD degree from the University of Auckland in 2019. Before joining Monash University, she was a Post-Doc researcher in the Large-Scale Data & Systems (LSDS) group in the Department of Computing at Imperial College London, UK. Her main research interests include applied cryptography, information security in cloud computing and distributed systems, trusted execution environments, side-channel attacks, and privacy-preserving machine learning.Dr. Shujie Cui is a Lecturer at Monash University in the Faculty of Information Technology. She obtained her PhD degree from the University of Auckland in 2019. Before joining Monash University, she was a Post-Doc researcher in the Large-Scale Data & Systems (LSDS) group in the Department of Computing at Imperial College London, UK. Her main research interests include applied cryptography, information security in cloud computing and distributed systems, trusted execution environments, side-channel attacks, and privacy-preserving machine learning.
时 间：2023年12月4日（周一）上午10：00 开始
地 点：番禺校区D1实验楼401会议室
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网络空间安全学院
2023年11月30日
