37 citations · 58 across the 9 of their papers we have counts for
4 papers · 1 filter
Label Inference Attacks against Node-level Vertical Federated GNNs
Marco Arazzi, Mauro Conti, Stefanos Koffas +4
Federated learning enables collaborative training of machine learning models by keeping the raw data of the involved workers private. Three of its main objectives are to improve th…
Rethinking the Trigger-injecting Position in Graph Backdoor Attack
Jing Xu, Gorka Abad, Stjepan Picek
Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such t…
Watermarking Graph Neural Networks based on Backdoor Attacks
Jing Xu, Stefanos Koffas, Oguzhan Ersoy +1
Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. Building a powerful GNN model is not a trivial task, as it requires a large amo…
Explainability-based Backdoor Attacks Against Graph Neural Networks
Jing Xu, Minhui, Xue +1
Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger-embedded inputs into an attacker-chosen target label while per…