4 citations · 8 across the 7 of their papers we have counts for
7 papers
model-based script synthesis for fuzzing
Zian Liu, Chao Chen, Muhammad Ejaz Ahmed +2
Kernel fuzzing is important for finding critical kernel vulnerabilities. Close-source (e.g., Windows) operating system kernel fuzzing is even more challenging due to the lack of so…
SemDiff: Binary Similarity Detection by Diffing Key-Semantics Graphs
Zian Liu, Zhi Zhang, Siqi Ma +6
Binary similarity detection is a critical technique that has been applied in many real-world scenarios where source code is not available, e.g., bug search, malware analysis, and c…
VulMatch: Binary-level Vulnerability Detection Through Signature
Zian Liu, Lei Pan, Chao Chen +4
Similar vulnerability repeats in real-world software products because of code reuse, especially in wildly reused third-party code and libraries. Detecting repeating vulnerabilities…
OptimShare: A Unified Framework for Privacy Preserving Data Sharing -- Towards the Practical Utility of Data with Privacy
M. A. P. Chamikara, Seung Ick Jang, Ian Oppermann +9
Tabular data sharing serves as a common method for data exchange. However, sharing sensitive information without adequate privacy protection can compromise individual privacy. Thus…
CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences
Shang Wang, Yansong Gao, Anmin Fu +4
As a critical threat to deep neural networks (DNNs), backdoor attacks can be categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and source-specific backdoor…
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches
Reena Zelenkova, Jack Swallow, M. A. P. Chamikara +5
Biometric data, such as face images, are often associated with sensitive information (e.g medical, financial, personal government records). Hence, a data breach in a system storing…
Local Differential Privacy for Federated Learning
M. A. P. Chamikara, Dongxi Liu, Seyit Camtepe +4
Advanced adversarial attacks such as membership inference and model memorization can make federated learning (FL) vulnerable and potentially leak sensitive private data. Local diff…