Machine Learning with Differential Privacy
GPTKB entity
Statements (44)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Machine Learning
Differential Privacy |
| gptkbp:hasCitations |
Computational Complexity
Balancing Privacy and Utility Model_Interpretability |
| gptkbp:hasFunding |
Increased Adoption
Public Awareness Improved Algorithms Regulatory Developments |
| gptkbp:hasRelatedPatent |
Artificial Intelligence
Statistical Analysis Data Protection |
| gptkbp:hasStaff |
Data Sharing
Regulatory Compliance Robustness Against Attacks Enhanced_User_Trust |
| https://www.w3.org/2000/01/rdf-schema#label |
Machine Learning with Differential Privacy
|
| gptkbp:isEvaluatedBy |
Accuracy
Scalability Privacy Loss Utility Loss |
| gptkbp:isLocatedIn |
gptkb:PyTorch
TensorFlow R Scikit-learn |
| gptkbp:isPromotedBy |
Academia
Governments Organizations Industry Leaders |
| gptkbp:isRelatedTo |
Data Mining
Algorithmic Fairness Secure Multi-Party Computation Privacy Preservation |
| gptkbp:isSupportedBy |
Conferences
Online Courses Research Papers Workshops |
| gptkbp:isUsedIn |
Finance
Healthcare Smart Cities Social_Networks |
| gptkbp:isVisitedBy |
gptkb:Microsoft
gptkb:Apple gptkb:Google |