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Privacy Preserving

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Showing 2176–2200 of 2975 papers

TitleStatusHype
Privacy-preserving Machine Learning for Medical Image Classification—0
CAPE: Context-Aware Private Embeddings for Private Language LearningCode0
Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating roomCode0
PIVODL: Privacy-preserving vertical federated learning over distributed labels—0
Mitigating Statistical Bias within Differentially Private Synthetic Data—0
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health—0
Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data—0
Learning Federated Representations and Recommendations with Limited Negatives—0
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular DataCode1
Towards Secure and Practical Machine Learning via Secret Sharing and Random PermutationCode0
Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning—0
Blockchain-based Trustworthy Federated Learning Architecture—0
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis—0
Effective and Privacy preserving Tabular Data Synthesizing—0
FedMatch: Federated Learning Over Heterogeneous Question Answering DataCode1
Privacy-Preserving Machine Learning: Methods, Challenges and Directions—0
FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning—0
Secure and Privacy-Preserving Federated Learning via Co-Utility—0
Anonymisation Models for Text Data: State of the art, Challenges and Future DirectionsCode0
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness—0
Secure Bayesian Federated Analytics for Privacy-Preserving Trend Detection—0
Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection—0
Fully Homomorphically Encrypted Deep Learning as a ServiceCode1
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep LearningCode0
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling—0
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