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Privacy Preserving

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Showing 751–800 of 2975 papers

TitleStatusHype
Blind Targeting: Personalization under Third-Party Privacy Constraints—0
Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization—0
Distributed Optimal Allocation with Quantized Communication and Privacy-Preserving Guarantees—0
Distributed generation of privacy preserving data with user customization—0
Distributed Layer-Partitioned Training for Privacy-Preserved Deep Learning—0
Blockchain as a Service: A Decentralized and Secure Computing Paradigm—0
Distributed Machine Learning and the Semblance of Trust—0
Distributed Online Learning with Multiple Kernels—0
Distributed Privacy-Preserving Electric Vehicle Charging Control Based on Secret Sharing—0
Distributed Private Online Learning for Social Big Data Computing over Data Center Networks—0
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching—0
Distributed Sketching Methods for Privacy Preserving Regression—0
Distribution Discrepancy Maximization for Image Privacy Preserving—0
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems—0
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences—0
DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering—0
Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?—0
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning—0
Do Gradient Inversion Attacks Make Federated Learning Unsafe?—0
Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images—0
Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment—0
An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications—0
Domain-incremental White Blood Cell Classification with Privacy-aware Continual Learning—0
A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures—0
Efficient Adaptive Federated Optimization of Federated Learning for IoT—0
Defense Against Gradient Leakage Attacks via Learning to Obscure Data—0
DP^2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring—0
DPCOVID: Privacy-Preserving Federated Covid-19 Detection—0
DPD-InfoGAN: Differentially Private Distributed InfoGAN—0
Bootstrap Model Aggregation for Distributed Statistical Learning—0
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information—0
DP-FP: Differentially Private Forward Propagation for Large Models—0
Defending Label Inference Attacks in Split Learning under Regression Setting—0
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling—0
A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Model—0
A Personal Privacy Preserving Framework: I Let You Know Who Can See What—0
Defending against Reconstruction Attack in Vertical Federated Learning—0
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities—0
Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbation—0
DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning—0
AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs—0
2P-DNN : Privacy-Preserving Deep Neural Networks Based on Homomorphic Cryptosystem—0
DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling—0
Efficient and Personalized Mobile Health Event Prediction via Small Language Models—0
Efficient Deep Learning on Multi-Source Private Data—0
DP-TrajGAN_ A privacy-aware trajectory generation model with differential privacy—0
dsLassoCov: a federated machine learning approach incorporating covariate control—0
DTW-SiameseNet: Dynamic Time Warped Siamese Network for Mispronunciation Detection and Correction—0
Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality—0
Auditing f-Differential Privacy in One Run—0
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