4 papers
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
Sayyed Farid Ahamed, Soumya Banerjee, Sandip Roy +7
Over the last few years, federated learning (FL) has emerged as a prominent method in machine learning, emphasizing privacy preservation by allowing multiple clients to collaborati…
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks
Marc Vucovich, Devin Quinn, Kevin Choi +3
Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. The main goal of a federated learning architec…
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning
Soumya Banerjee, Sandip Roy, Sayyed Farid Ahamed +7
The membership inference attack (MIA) is a popular paradigm for compromising the privacy of a machine learning (ML) model. MIA exploits the natural inclination of ML models to over…
Foundational Models for Malware Embeddings Using Spatio-Temporal Parallel Convolutional Networks
Dhruv Nandakumar, Devin Quinn, Elijah Soba +6
In today's interconnected digital landscape, the proliferation of malware poses a significant threat to the security and stability of computer networks and systems worldwide. As th…