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Found 7 papers, 1 papers with code
Differentially Private Heavy Hitter Detection using Federated Analytics
In this work, we study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection.
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns.
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware
DarKnight relies on cooperative execution between trusted execution environments (TEE) and accelerators, where the TEE provides privacy and integrity verification, while accelerators perform the bulk of the linear algebraic computation to optimize the performance.
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings
To assess the information leakage of SER systems trained using FL, we propose an attribute inference attack framework that infers sensitive attribute information of the clients from shared gradients or model parameters, corresponding to the FedSGD and the FedAvg training algorithms, respectively.
Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
Much of the overhead in prior schemes comes from the fact that they tightly couple coding for all three problems into a single framework.
Byzantine-Robust and Privacy-Preserving Framework for FedML
This learning setting presents, among others, two unique challenges: how to protect privacy of the clients' data during training, and how to ensure integrity of the trained model.
Privacy and Integrity Preserving Training Using Trusted Hardware
Privacy and security-related concerns are growing as machine learning reaches diverse application domains.
