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Privacy Preserving

Papers

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Showing 2001–2050 of 2975 papers

TitleStatusHype
SoK: What Makes Private Learning Unfair?—0
Source Anonymity for Private Random Walk Decentralized Learning—0
Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0—0
Sparsified Secure Aggregation for Privacy-Preserving Federated Learning—0
Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data—0
Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces—0
Speed-up of Data Analysis with Kernel Trick in Encrypted Domain—0
SPEFL: Efficient Security and Privacy Enhanced Federated Learning Against Poisoning Attacks—0
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics—0
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability—0
Split Two-Tower Model for Efficient and Privacy-Preserving Cross-device Federated Recommendation—0
SPRITE: A Scalable Privacy-Preserving and Verifiable Collaborative Learning for Industrial IoT—0
sqSGD: Locally Private and Communication Efficient Federated Learning—0
SSNet: A Lightweight Multi-Party Computation Scheme for Practical Privacy-Preserving Machine Learning Service in the Cloud—0
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection—0
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems—0
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey—0
Statistical and Topological Properties of Gaussian Smoothed Sliced Probability Divergences—0
ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation—0
Step length measurement in the wild using FMCW radar—0
Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving—0
Stochastic Client Selection for Federated Learning with Volatile Clients—0
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design—0
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees—0
STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition—0
Stratified cross-validation for unbiased and privacy-preserving federated learning—0
Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression—0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors—0
Study of the performance and scalability of federated learning for medical imaging with intermittent clients—0
Sub-Linear Privacy-Preserving Near-Neighbor Search—0
Submodular Combinatorial Information Measures with Applications in Machine Learning—0
Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning—0
Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images—0
Successive Point-of-Interest Recommendation with Local Differential Privacy—0
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference—0
Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning—0
Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications—0
Survey of Privacy-Preserving Collaborative Filtering—0
S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning—0
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning—0
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees—0
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning—0
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework—0
Switch-Based Multi-Part Neural Network—0
SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers—0
Synergising Hierarchical Data Centers and Power Networks: A Privacy-Preserving Approach—0
SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Mining—0
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications—0
Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs—0
Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions—0
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