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Found 8 papers, 2 papers with code
Vertical Federated Learning: Taxonomies, Threats, and Prospects
In a number of practical scenarios, VFL is more relevant than HFL as different companies (e. g., bank and retailer) hold different features (e. g., credit history and shopping history) for the same set of customers.
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label
In this paper, we propose BadHash, the first generative-based imperceptible backdoor attack against deep hashing, which can effectively generate invisible and input-specific poisoned images with clean label.
SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service
With the proliferation of cloud computing, it is increasingly popular to deploy the services of complex and resource-intensive model training and inference in the cloud due to its prominent benefits.
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization
In this paper, we present a system design which offers efficient protection of individual model updates throughout the learning procedure, allowing clients to only provide obscured model updates while a cloud server can still perform the aggregation.
NTD: Non-Transferability Enabled Backdoor Detection
A backdoor deep learning (DL) model behaves normally upon clean inputs but misbehaves upon trigger inputs as the backdoor attacker desires, posing severe consequences to DL model deployments.
RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things
To this end, a 1-bit quantized DNN model or deep binary neural network maximizes the memory efficiency, where each parameter in a BNN model has only 1-bit.
Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks
To corroborate the efficiency of Decamouflage, we have also measured its run-time overhead on a personal PC with an i5 CPU and found that Decamouflage can detect image-scaling attacks in milliseconds.
Evaluation of Federated Learning in Phishing Email Detection
For a fixed total email dataset, the global RNN based model suffers by a 1. 8% accuracy drop when increasing organizational counts from 2 to 10.
