Calibrating Noise to Sensitivity in Private Data Analysis

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

• Machine learning: Trends, perspectives, and prospects→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Deep Learning with Differential Privacy→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Advances and Open Problems in Federated Learning→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Differential Privacy→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Federated Learning: Challenges, Methods, and Future Directions→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Membership Inference Attacks Against Machine Learning Models→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• The Algorithmic Foundations of Differential Privacy→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Fairness through awareness→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Differential Privacy: A Survey of Results→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Robust De-anonymization of Large Sparse Datasets→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• On the Opportunities and Risks of Foundation Models→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Our Data, Ourselves: Privacy Via Distributed Noise Generation→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Privacy-preserving data publishing→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• RAPPOR→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
• Calibrating Noise to Sensitivity in Private Data Analysis→k-ANONYMITY: A MODEL FOR PROTECTING PRIVACYLegacy edge — source detail unavailable
• Calibrating Noise to Sensitivity in Private Data Analysis→Privacy-preserving data miningLegacy edge — source detail unavailable
• Our Data, Ourselves: Privacy Via Distributed Noise Generation→Privacy-preserving data miningLegacy edge — source detail unavailable
• RAPPOR→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
• The Algorithmic Foundations of Differential Privacy→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
• The Algorithmic Foundations of Differential Privacy→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
• The Algorithmic Foundations of Differential Privacy→Differential PrivacyLegacy edge — source detail unavailable
• Fairness through awareness→Differential PrivacyLegacy edge — source detail unavailable
• Differential Privacy: A Survey of Results→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
• Differential Privacy: A Survey of Results→Privacy-preserving data miningLegacy edge — source detail unavailable
• Differential Privacy: A Survey of Results→k-ANONYMITY: A MODEL FOR PROTECTING PRIVACYLegacy edge — source detail unavailable
• Differential Privacy: A Survey of Results→Differential PrivacyLegacy edge — source detail unavailable
• Robust De-anonymization of Large Sparse Datasets→Privacy-preserving data miningLegacy edge — source detail unavailable
• Robust De-anonymization of Large Sparse Datasets→k-ANONYMITY: A MODEL FOR PROTECTING PRIVACYLegacy edge — source detail unavailable
• Robust De-anonymization of Large Sparse Datasets→Differential PrivacyLegacy edge — source detail unavailable
• Privacy-preserving data publishing→Differential Privacy: A Survey of ResultsLegacy edge — source detail unavailable
• Privacy-preserving data publishing→Privacy-preserving data miningLegacy edge — source detail unavailable
• Privacy-preserving data publishing→k-ANONYMITY: A MODEL FOR PROTECTING PRIVACYLegacy edge — source detail unavailable
• Privacy-preserving data publishing→Differential PrivacyLegacy edge — source detail unavailable
• Deep Learning with Differential Privacy→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
• Deep Learning with Differential Privacy→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
• Deep Learning with Differential Privacy→Privacy-preserving data miningLegacy edge — source detail unavailable
• Deep Learning with Differential Privacy→k-ANONYMITY: A MODEL FOR PROTECTING PRIVACYLegacy edge — source detail unavailable
• Membership Inference Attacks Against Machine Learning Models→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
• Differential Privacy→Privacy-preserving data miningLegacy edge — source detail unavailable
• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
• Advances and Open Problems in Federated Learning→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
• Advances and Open Problems in Federated Learning→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
• Advances and Open Problems in Federated Learning→Fairness through awarenessLegacy edge — source detail unavailable
• Advances and Open Problems in Federated Learning→Differential Privacy: A Survey of ResultsLegacy edge — source detail unavailable
• Advances and Open Problems in Federated Learning→Privacy-preserving data miningLegacy edge — source detail unavailable
• Advances and Open Problems in Federated Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
• Advances and Open Problems in Federated Learning→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
• On the Opportunities and Risks of Foundation Models→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable