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A differential privacy‐preserving deep learning caching framework for heterogeneous communication network systems
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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An optimal (∊,δ) -differentially private learning of distributed deep fuzzy models
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EdgeSanitizer: Locally Differentially Private Deep Inference at the Edge for Mobile Data Analytics
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Hierarchical Edge Caching in Device-to-Device Aided Mobile Networks: Modeling, Optimization, and Design
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Proactive Retention-Aware Caching With Multi-Path Routing for Wireless Edge Networks
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Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching
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Smart-Edge-CoCaCo: AI-Enabled Smart Edge with Joint Computation, Caching, and Communication in Heterogeneous IoT
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Security and Privacy for Mobile Edge Caching: Challenges and Solutions
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Membership Inference Attacks Against Machine Learning Models
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NPMML: A Framework for Non-interactive Privacy-preserving Multi-party Machine Learning
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Efficient and Secure Outsourcing of Differentially Private Data Publishing With Multiple Evaluators
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Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments
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Privacy-Preserving Collaborative Deep Learning With Unreliable Participants
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Game Theory and Reinforcement Learning Based Secure Edge Caching in Mobile Social Networks
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D2D Communications in Heterogeneous Networks With Full-Duplex Relays and Edge Caching
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MAS-Encryption and its Applications in Privacy-Preserving Classifiers
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The Dynamic Privacy-Preserving Mechanisms for Online Dynamic Social Networks
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GATrust: A Multi-Aspect Graph Attention Network Model for Trust Assessment in OSNs
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inferredcites →https://doi.org/10.1109/tsmc.2019.2952539openalex-snapshot
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Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks
inferredcites →https://doi.org/10.1109/tvt.2020.2975559openalex-snapshot
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inferredcites →https://doi.org/10.1109/tvt.2020.3001203openalex-snapshot
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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
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