most citedLocal Differential Privacy for Federated Learning

4 citations · 8 across the 7 of their papers we have counts for

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cs.CR2023

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…

cs.CR2023

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…

cs.CR2023

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…

cs.CR2023

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…

cs.CR2022★ 2 cited

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…

cs.CR2022★ 4 cited

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…