Differential Privacy: A Survey of Results
No abstract in the source record.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
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 |
| L -diversity | 2007 | 3,628 |
| 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 |
| Our Data, Ourselves: Privacy Via Distributed Noise Generation | 2006 | 1,826 |
| Privacy-preserving data mining | 2000 | 1,709 |
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Federated Machine Learning | 2019 | 6,228 |
| Advances and Open Problems in Federated Learning | 2020 | 5,383 |
| A survey on federated learning | 2021 | 1,847 |
| Privacy-preserving data publishing | 2010 | 1,651 |
Links
Topics
| Privacy-Preserving Technologies in Data | Computer Science |
| Cryptography and Data Security | Computer Science |
| Random Matrices and Applications | Mathematics |
Is this record sound?
complete
Nothing in this record contradicts itself and no field we check is missing.
- supports1 author record(s) attached.
- supports50 reference(s) recorded.
- neutralThe DOI carries no year to check against.
- supportsA title is present.
Provenance
sha256 a08467ae9504f237…