326 citations · 610 across the 13 of their papers we have counts for
13 papers
Characterizing Trust Boundary Vulnerabilities in TEE Containers: An Empirical Study
Weijie Liu, Hongbo Chen, Shuo Huai +7
Trusted Execution Environments (TEEs) have become a cornerstone of confidential computing, attracting significant attention from academia and industry. To support secure and scalab…
The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks
Xiaoyi Chen, Siyuan Tang, Rui Zhu +7
The rapid advancements of large language models (LLMs) have raised public concerns about the privacy leakage of personally identifiable information (PII) within their extensive tra…
Large Language Model Soft Ideologization via AI-Self-Consciousness
Xiaotian Zhou, Qian Wang, Xiaofeng Wang +2
Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, few studies have addressed the LLM threat and vulnerab…
Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering
Rui Zhu, Di Tang, Siyuan Tang +4
Most existing methods to detect backdoored machine learning (ML) models take one of the two approaches: trigger inversion (aka. reverse engineer) and weight analysis (aka. model di…
Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models
Rui Zhu, Di Tang, Siyuan Tang +2
In this paper, we present a simple yet surprisingly effective technique to induce "selective amnesia" on a backdoored model. Our approach, called SEAM, has been inspired by the pro…
Understanding Impacts of Task Similarity on Backdoor Attack and Detection
Di Tang, Rui Zhu, XiaoFeng Wang +2
With extensive studies on backdoor attack and detection, still fundamental questions are left unanswered regarding the limits in the adversary's capability to attack and the defend…
Understanding TEE Containers, Easy to Use? Hard to Trust
Weijie Liu, Hongbo Chen, XiaoFeng Wang +4
As an emerging technique for confidential computing, trusted execution environment (TEE) receives a lot of attention. To better develop, deploy, and run secure applications on a TE…
HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework
Chathura Widanage, Weijie Liu, Jiayu Li +4
Trusted execution environments (TEE) such as Intel's Software Guard Extension (SGX) have been widely studied to boost security and privacy protection for the computation of sensiti…
Confidential Attestation: Efficient in-Enclave Verification of Privacy Policy Compliance
Weijie Liu, Wenhao Wang, Xiaofeng Wang +9
A trusted execution environment (TEE) such as Intel Software Guard Extension (SGX) runs a remote attestation to prove to a data owner the integrity of the initial state of an encla…
Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection
Di Tang, XiaoFeng Wang, Haixu Tang +1
A security threat to deep neural networks (DNN) is backdoor contamination, in which an adversary poisons the training data of a target model to inject a Trojan so that images carry…
Toward Scalable Fully Homomorphic Encryption Through Light Trusted Computing Assistance
Wenhao Wang, Yichen Jiang, Qintao Shen +8
It has been a long standing problem to securely outsource computation tasks to an untrusted party with integrity and confidentiality guarantees. While fully homomorphic encryption…
Understanding Membership Inferences on Well-Generalized Learning Models
Yunhui Long, Vincent Bindschaedler, Lei Wang +5
Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model. Prior work has shown that the attack is fea…
Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGX
Wenhao Wang, Guoxing Chen, Xiaorui Pan +5
Side-channel risks of Intel's SGX have recently attracted great attention. Under the spotlight is the newly discovered page-fault attack, in which an OS-level adversary induces pag…