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Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Implementation of Membership Inference Attack Affecting Federated Learning-based Anomaly Detection System |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | This thesis investigates the data privacy preservation capabilities of Federated Learning (FL), specifically focusing on Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL) settings. Despite their existing data privacy advantages, both CFL and DFL have been shown to be vulnerable to adversarial attacks, including Membership Inference Attacks (MIA). This thesis compares the data privacy-preserving capabilities of CFL and DFL, trained on MNIST, FashionMNIST, and CIFAR-10, against White-Box and Black-Box MIA across various performance metrics. Furthermore, the most commonly used defense techniques used against MIA are discussed, such as Differential Privacy (DP), Regularization, and Knowledge Distillation. The findings suggest that FL models generally provide better data privacy than ML models, with CFL being the best data privacy preserving federation against shadow models using binary classifier-based MIA and DFL models with a fully connected network topology, showing strong resistance against MIA using a prediction-based classifier. This work offers valuable insights into the data privacy-preserving abilities of CFL and DFL in different scenarios and underlines the importance of further research in the domain of data privacy in collaborative ML. |
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