Privacy-Preserving Techniques for Data Analysis and Machine Learning (Q168565): Difference between revisions
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Revision as of 18:33, 24 June 2024
This cluster of papers focuses on privacy-preserving techniques for data analysis and machine learning, including topics such as differential privacy, federated learning, k-anonymity, secure computation, and location privacy
- Differential Privacy
- Federated Learning
- k-Anonymity
- Privacy Preservation
- Location Privacy
- Data Mining
- Anonymization
- Secure Computation
- Membership Inference Attacks
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Privacy-Preserving Techniques for Data Analysis and Machine Learning |
This cluster of papers focuses on privacy-preserving techniques for data analysis and machine learning, including topics such as differential privacy, federated learning, k-anonymity, secure computation, and location privacy |
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