Research Interests

Our team designs and develops secure and efficient computer systems for high intelligence and automation. We research and explore security and privacy issues and solutions in various systems. We also build large-scale distributed systems for emerging applications. We have been working on the following topics:

Deep learning security.

The emerging deep learning technology has been widely commercialized in our daily life. However, it also introduces new security and privacy threats, which could bring disastrous consequences in critical scenarios. Our goal is to investigate the security vulnerabilities as well as defense solutions for various deep learning systems, such as computer vision, natural language processing, reinforcement learning, distributed federated learning, etc. We are interested in the following security problems.

• Large Language Model and Multi-modal Model Security [NDSS24a, NDSS24b, S&P24]
• Adversarial examples [ECCV22, MM22-b, TBD22-b, AAAI20]
• DNN backdoor and bit flip attacks [ICLR24-c, Usenix Security23, ICCV23-a, ICCV23-b, CVPR23, ICLR23-b, AAAI23, ACL23-a, TDSC22-b, TDSC23-d, NAACL22, MM22-a, ICLR22-b, AsiaCCS21-b]
• Model and data privacy (model extraction, model inversion, membership inference) [ICLR24-a, TDSC23-a, ICLR23-a, ICLR22-a, AsiaCCS21-a, ACSAC19]
• DNN IP protection (watermarking, fingerprinting) [TCSVT22-a, IJCAI21, AAMAS21, CVPR19]
• Federated and decentralized learning [TPAMI23, TIFS23-b, TCSVT23, TBD22-a, TCSVT22-b, TCSVT22-c, CVPR21]
• Privacy-preserving machine learning with cryptography (homomorphic encryption, secure multi-party encryption) [Usenix Security24-a, Usenix Security24-b, ICML23, TDSC23-b, TDSC23-c, TIFS23-a, TDSC22-a, TIFS22-c, NeurIPS22, ACSAC21]

Benefiting from the advances in mechanics, sensors, artificial intelligence and networked systems, a variety of powerful robots have been designed to perform different tasks autonomously, ranging from space exploration to daily life housework to manufacturing. However, the complexity in the robot systems inevitably enlarges the attack surface, and brings new security challenges in the design and development of robot applications. Our group aims to explore the security problems at different layers of robotic and autonomous driving systems, and possible mitigation solutions.

• Attacks against perception systems (sensor spoofing, backdoor) [MM22-a, TITS23-a]
• Robot Operation System security [CCS22-a, RAID22-a, TDSC24-a ]
• Cloud-robotic and multi-robotic system security [RAID22-b, InfS21, IPDPS21]
• Access control in autonomous and robotic systems [TITS23-b, TITS23-c, TIFS22-a]

Modern deep learning models are becoming more complex with huge production and deployment cost. Consequently, IT corporations, research institutes and cloud providers build large-scale GPU clusters to ease the development of DL training and inference jobs. It is important to schedule these jobs and allocate the valuable resources in an efficient and scalable way. We are interested in designing new systems and frameworks to manage, optimize and accelerate deep learning workloads in large-scale datacenters.

• Survey of deep learning workload scheduling [CSUR24]
• Deep learning training workload scheduling [NSDI24, OSDI23, ICCD22, SoCC21, TPDS22, SC21]
• Interpretable machine learning systems [ASPLOS23, ATC22]
• Efficient machine learning at edge [WWW24, ICLR24-b]
• Graph Neural Network optimization [ICDE24]

Infrastructure-as-a-Service (IaaS) clouds provide computation and storage services to enterprises and individuals with increased elasticity and low cost. Cloud customers rent resources in the form of virtual machines (VMs). However, these VMs may face various security threats. We build security-aware computer architectures to attest and protect the security of cloud applications. We also design new methodologies to assess and mitigate micro-architectural side-channel attacks.