CAREER: Foundations of Privacy-Preserving Collaborative Learning — NSF Award to University of California-Riverside (CA, $540,956)
Collaborative machine-learning techniques allow multiple data owners to collaborate to train better machine-learning models by increasing the volume and diversity of data. In many real-world scenarios, however, the data is privacy-sensitive, as is the case for healthcare records, financial transactions, or geolocation
| Award title | CAREER: Foundations of Privacy-Preserving Collaborative Learning |
|---|---|
| Award ID | 2144927 |
| Awardee | University of California-Riverside |
| City | RIVERSIDE |
| State | CA |
| Amount obligated | $540,956 |
| Principal investigator | Basak Guler |
| Program | Comm & Information Foundations |
| Start date | 03/01/2022 |
| Abstract | Collaborative machine-learning techniques allow multiple data owners to collaborate to train better machine-learning models by increasing the volume and diversity of data. In many real-world scenarios, however, the data is privacy-sensitive, as is the case for healthcare records, financial transactions, or geolocation data. Privacy-preserving machine-learning techniques can facilitate machine-learning applications while protecting the privacy of sensitive data. This project aims to develop an ef |
| Source | NSF Awards |
$799/mo
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