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- research-articleNovember 2023
Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation
CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications SecurityPages 2651–2664https://doi.org/10.1145/3576915.3623165Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node embeddings for various graph inference tasks, but training them can raise privacy concerns. To address this, we propose using ...
- abstractJune 2023
Link Local Differential Privacy in GNNs via Bayesian Estimation
SIGMOD '23: Companion of the 2023 International Conference on Management of DataPages 265–267https://doi.org/10.1145/3555041.3589398Recent years have witnessed the emergence of graph neural networks (GNNs) and an increasing amount of attention on GNNs from the data management community. Yet, training GNNs may raise privacy concerns as they may reveal sensitive information that must ...