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Privacy Preserving

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Showing 1151–1200 of 2975 papers

TitleStatusHype
A Novel Multimodal System to Predict Agitation in People with Dementia Within Clinical Settings: A Proof of Concept—0
Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning—0
A compressive multi-kernel method for privacy-preserving machine learning—0
Distributed Coordination of Deferrable Loads: A Real-time Market with Self-fulfilling Forecasts—0
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks—0
Distinguish Then Exploit: Source-free Open Set Domain Adaptation via Weight Barcode Estimation and Sparse Label Assignment—0
Distilling On-Device Intelligence at the Network Edge—0
Blind quantum machine learning with quantum bipartite correlator—0
Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation—0
Blind Federated Learning without initial model—0
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning—0
BLAZE: Blazing Fast Privacy-Preserving Machine Learning—0
A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss—0
Disentangling the Linguistic Competence of Privacy-Preserving BERT—0
DiffUMI: Training-Free Universal Model Inversion via Unconditional Diffusion for Face Recognition—0
Difficulty in estimating visual information from randomly sampled images—0
Differential Private Stack Generalization with an Application to Diabetes Prediction—0
Birds Eye View Social Distancing Analysis System—0
Another Use of SMOTE for Interpretable Data Collaboration Analysis—0
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation—0
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection—0
Differential Privacy Under Class Imbalance: Methods and Empirical Insights—0
Differential Privacy Overview and Fundamental Techniques—0
Binary Mechanisms under Privacy-Preserving Noise—0
A note on privacy preserving iteratively reweighted least squares—0
Adversarial Test on Learnable Image Encryption—0
ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning—0
Differential Privacy of Quantum and Quantum-Inspired-Classical Recommendation Algorithms—0
Binary Federated Learning with Client-Level Differential Privacy—0
A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings—0
Differential Privacy May Have a Potential Optimization Effect on Some Swarm Intelligence Algorithms besides Privacy-preserving—0
Bilevel Optimization for Differentially Private Optimization in Energy Systems—0
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation—0
Differential privacy guarantees of Markov chain Monte Carlo algorithms—0
Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction—0
Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference—0
Anonymizing medical case-based explanations through disentanglement—0
Differential Privacy and Machine Learning: a Survey and Review—0
Mitigating Statistical Bias within Differentially Private Synthetic Data—0
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey—0
Differentially Private Wireless Federated Learning Using Orthogonal Sequences—0
Bias and Variance of Post-processing in Differential Privacy—0
Differentially Private Variational Dropout—0
Differentially Private Training of Mixture of Experts Models—0
Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders—0
BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models—0
BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse—0
Anonymizing Data for Privacy-Preserving Federated Learning—0
Differentially Private Submodular Maximization: Data Summarization in Disguise—0
Differentially Private Stochastic Gradient Descent with Low-Noise—0
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