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Privacy Preserving

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Showing 1701–1725 of 2975 papers

TitleStatusHype
CrowdGuard: Federated Backdoor Detection in Federated LearningCode0
Mitigating Unintended Memorization in Language Models via Alternating Teaching—0
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition—0
Differentially Private Bootstrap: New Privacy Analysis and Inference StrategiesCode0
Momentum Aggregation for Private Non-convex ERM—0
PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt LearningCode1
mPSAuth: Privacy-Preserving and Scalable Authentication for Mobile Web Applications—0
Synthetic Dataset Generation for Privacy-Preserving Machine Learning—0
Federated Boosted Decision Trees with Differential PrivacyCode1
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search—0
Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption—0
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving DesensitizationCode2
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms—0
Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition—0
Privacy-Preserving Feature Coding for Machines—0
Heterogeneous Graph Neural Network for Privacy-Preserving RecommendationCode1
BETOLD: A Task-Oriented Dialog Dataset for Breakdown Detection—0
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph—0
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning—0
Differentially Private Optimization on Large Model at Small CostCode1
Differentially Private Bias-Term Fine-tuning of Foundation ModelsCode1
On the Impossible Safety of Large AI Models—0
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction—0
PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems—0
Joint Optimization of Energy Consumption and Completion Time in Federated Learning—0
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