# Privacy-Preserving Technologies in Data

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/privacy-preserving-technologies-in-data-2/

## Facts

| Field | Value |
| --- | --- |
| Citations | 1,057,290 |
| Description | 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. The papers explore methods to protect sensitive information while performing data mining, machine learning, and statistical analysis. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | https://openalex.org/T10764 |
| Works | 85,844 |

## Topic researchers

Showing 12 of 20.

- [Geoffrey E. Hinton](https://scholariq.org/researchers/geoffrey-e-hinton/)
- [Peer Bork](https://scholariq.org/researchers/peer-bork/)
- [Li Fei-Fei](https://scholariq.org/researchers/li-fei-fei/)
- [Ilya Sutskever](https://scholariq.org/researchers/ilya-sutskever/)
- [Michael I. Jordan](https://scholariq.org/researchers/michael-i-jordan/)
- [Stefan Schreiber](https://scholariq.org/researchers/stefan-schreiber/)
- [H. Vincent Poor](https://scholariq.org/researchers/h-vincent-poor/)
- [Anil K. Jain](https://scholariq.org/researchers/anil-k-jain/)
- [Mihai G. Netea](https://scholariq.org/researchers/mihai-g-netea/)
- [Philip S. Yu](https://scholariq.org/researchers/philip-s-yu/)
- [Hadley Wickham](https://scholariq.org/researchers/hadley-wickham/)
- [Léon Bottou](https://scholariq.org/researchers/leon-bottou/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.