Robust De-anonymization of Large Sparse Datasets
Arvind Narayanan, Vitaly Shmatikov
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Citation edge evidence
- The Algorithmic Foundations of Differential Privacy→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
- Practical Secure Aggregation for Privacy-Preserving Machine Learning→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
- Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Robust De-anonymization of Large Sparse DatasetsLegacy 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→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
- Robust De-anonymization of Large Sparse Datasets→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→L-diversity: privacy beyond k-anonymityLegacy edge — source detail unavailable
- Robust De-anonymization of Large Sparse Datasets→ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSIONLegacy edge — source detail unavailable
- Robust De-anonymization of Large Sparse Datasets→Privacy-preserving data miningLegacy edge — source detail unavailable
- Calibrating Noise to Sensitivity in Private Data Analysis→Privacy-preserving data miningLegacy edge — source detail unavailable
- Calibrating Noise to Sensitivity in Private Data Analysis→k-ANONYMITY: A MODEL FOR PROTECTING PRIVACYLegacy edge — source detail unavailable
- The Algorithmic Foundations of Differential Privacy→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
- The Algorithmic Foundations of Differential Privacy→Differential PrivacyLegacy edge — source detail unavailable
- Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures→Differential PrivacyLegacy edge — source detail unavailable
- ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION→k-ANONYMITY: A MODEL FOR PROTECTING PRIVACYLegacy edge — source detail unavailable
- L-diversity: privacy beyond k-anonymity→Privacy-preserving data miningLegacy edge — source detail unavailable
- L-diversity: privacy beyond k-anonymity→k-ANONYMITY: A MODEL FOR PROTECTING PRIVACYLegacy edge — source detail unavailable
- Practical Secure Aggregation for Privacy-Preserving Machine Learning→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Practical Secure Aggregation for Privacy-Preserving Machine Learning→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
- Practical Secure Aggregation for Privacy-Preserving Machine Learning→Differential PrivacyLegacy edge — source detail unavailable
- Differential Privacy→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
- Differential Privacy→ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSIONLegacy edge — source detail unavailable
- Differential Privacy→Privacy-preserving data miningLegacy edge — source detail unavailable
- Differential Privacy→Privacy-preserving data miningLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Secure, privacy-preserving and federated machine learning in medical imaging→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Differential PrivacyLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY | 2002 | 8,585 |
| Calibrating Noise to Sensitivity in Private Data Analysis | 2006 | 7,210 |
| Differential Privacy | 2006 | 5,337 |
| Privacy-preserving data mining | 2000 | 2,997 |
| L-diversity: privacy beyond k-anonymity | 2006 | 2,385 |
| ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION | 2002 | 1,982 |
| Privacy-preserving data mining | 2000 | 1,709 |
What cites it, inside the corpus
Links
Topics
| Privacy-Preserving Technologies in Data | Computer Science |
| Internet Traffic Analysis and Secure E-voting | Computer Science |
| Privacy, Security, and Data Protection | Social Sciences |
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