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Privacy Preserving

Papers

Showing 19762000 of 2975 papers

TitleStatusHype
Differential privacy for symmetric log-concave mechanismsCode0
FlowSense: Monitoring Airflow in Building Ventilation Systems Using Audio SensingCode0
Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction Study0
Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and Cholesterol DatasetCode0
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy0
Fair Division with Money and Prices0
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices0
SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service0
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey0
Privacy Preserving Visual Question Answering0
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationCode1
UA-FedRec: Untargeted Attack on Federated News RecommendationCode1
Do Gradient Inversion Attacks Make Federated Learning Unsafe?0
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data ReleaseCode0
Privacy-preserving Generative Framework Against Membership Inference Attacks0
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation0
Backpropagation Clipping for Deep Learning with Differential PrivacyCode0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection0
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees0
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
PrivFair: a Library for Privacy-Preserving Fairness AuditingCode0
CECILIA: Comprehensive Secure Machine Learning FrameworkCode0
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks0
Lossy Compression of Noisy Data for Private and Data-Efficient Learning0
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