1 citations · 2 across the 13 of their papers we have counts for
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Do Defenses Against LLM Extraction Work Across Attacks? A Lifecycle Benchmark of Black-Box Model Extraction
Shuze Liu, Kaixiang Zhao, Runyang Xu +4
Large language models (LLMs) deployed through text-only APIs face model extraction risks, as adversaries can collect their responses to train surrogates that reproduce their capabi…
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
Kaixiang Zhao, Bolin Shen, Yuyang Dai +2
Graph neural networks (GNNs) deployed as cloud services can be stolen through model-extraction attacks, which train a surrogate from query responses to reproduce the target's behav…
Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses
Lincan Li, Bolin Shen, Chenxi Zhao +4
Graph-structured data, which captures non-Euclidean relationships and interactions between entities, is growing in scale and complexity. As a result, training state-of-the-art grap…
A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
Kaixiang Zhao, Lincan Li, Kaize Ding +3
Machine learning (ML) models have significantly grown in complexity and utility, driving advances across multiple domains. However, substantial computational resources and speciali…
DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
Kaixiang Zhao, Joseph Yousry Attalla, Qian Lou +1
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such…
A Survey on Model Extraction Attacks and Defenses for Large Language Models
Kaixiang Zhao, Lincan Li, Kaize Ding +3
Model extraction attacks pose significant security threats to deployed language models, potentially compromising intellectual property and user privacy. This survey provides a comp…
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments
Kaixiang Zhao, Lincan Li, Kaize Ding +3
Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data. With the increas…