SOTAVerified

Privacy Preserving

Papers

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Showing 1451–1500 of 2975 papers

TitleStatusHype
To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?—0
Toward a Robust Diversity-Based Model to Detect Changes of Context—0
Toward Privacy and Utility Preserving Image Representation—0
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion—0
Towards a Data Privacy-Predictive Performance Trade-off—0
Towards a More Reliable Privacy-preserving Recommender System—0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services—0
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence—0
Towards a User Privacy-Aware Mobile Gaming App Installation Prediction Model—0
Towards Automated Homomorphic Encryption Parameter Selection with Fuzzy Logic and Linear Programming—0
Towards autonomic orchestration of machine learning pipelines in future networks—0
Towards Causal Federated Learning For Enhanced Robustness and Privacy—0
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation—0
Towards Differentially Private Truth Discovery for Crowd Sensing Systems—0
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data—0
Towards End-to-End Private Automatic Speaker Recognition—0
Towards Everyday Virtual Reality through Eye Tracking—0
Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning—0
Towards Fast and Scalable Private Inference—0
Towards federated multivariate statistical process control (FedMSPC)—0
Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning—0
Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study—0
Towards Graph-Based Privacy-Preserving Federated Learning: ModelNet -- A ResNet-based Model Classification Dataset—0
Towards hyperparameter-free optimization with differential privacy—0
Towards Personalized Federated Learning—0
Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture—0
Towards Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models—0
Towards privacy-preserving cooperative control via encrypted distributed optimization—0
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning—0
Towards Privacy-preserving Explanations in Medical Image Analysis—0
Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions—0
Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture—0
Towards Privacy-Preserving Neural Architecture Search—0
Towards Privacy-Preserving Person Re-identification via Person Identify Shift—0
Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models—0
Towards Private Learning on Decentralized Graphs with Local Differential Privacy—0
Towards Real-time Drowsiness Detection for Elderly Care—0
Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning—0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis—0
Towards Robust Federated Learning via Logits Calibration on Non-IID Data—0
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design—0
Towards Scalable Wireless Federated Learning: Challenges and Solutions—0
Towards Split Learning-based Privacy-Preserving Record Linkage—0
Towards Transactive Energy: An Analysis of Information-related Practical Issues—0
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives—0
Towards Understanding the Impact of Model Size on Differential Private Classification—0
Towards Unified Modeling in Federated Multi-Task Learning via Subspace Decoupling—0
Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation—0
Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning—0
Training Differentially Private Graph Neural Networks with Random Walk Sampling—0
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