Pasin Manurangsi

Person

Bangkok, TH

People

Patents Applicationslast 30 patents

Information Patent Application
End to End Protocol of Frequency Estimation with Multi-Party Comput...
- Publication number 20250232058
- Publication date Jul 17, 2025
- Google LLC
- Badih Ghazi
- G06 - COMPUTING CALCULATING COUNTING
Information Patent Application
Learning with Label Differential Privacy via Projections
- Publication number 20250190847
- Publication date Jun 12, 2025
- Google LLC
- Badih Ghazi
- G06 - COMPUTING CALCULATING COUNTING
Information Patent Application
ADAPTIVE PRIVACY-PRESERVING INFORMATION RETRIEVAL
- Publication number 20250139282
- Publication date May 1, 2025
- Google LLC
- Pasin Manurangsi
- G06 - COMPUTING CALCULATING COUNTING
Information Patent Application
PRIVACY SENSITIVE ESTIMATION OF DIGITAL RESOURCE ACCESS FREQUENCY
- Publication number 20250131112
- Publication date Apr 24, 2025
- Google LLC
- Pasin Manurangsi
- G06 - COMPUTING CALCULATING COUNTING
Information Patent Application
Adaptive Privacy Budgeting and Adaptive Sampling Value Prediction
- Publication number 20250005179
- Publication date Jan 2, 2025
- Google LLC
- Jiayu Peng
- G06 - COMPUTING CALCULATING COUNTING
Information Patent Application
Training Machine-Learned Models with Label Differential Privacy
- Publication number 20240265294
- Publication date Aug 8, 2024
- Google LLC
- Badih Ghazi
- G06 - COMPUTING CALCULATING COUNTING
Information Patent Application
Distributed, Private, Sparse Histograms in the Two-Server Model
- Publication number 20230327850
- Publication date Oct 12, 2023
- Google LLC
- Badih Ghazi
- H04 - ELECTRIC COMMUNICATION TECHNIQUE

Pasin Manurangsi

Person

Patents Applicationslast 30 patents

End to End Protocol of Frequency Estimation with Multi-Party Comput...

Learning with Label Differential Privacy via Projections

ADAPTIVE PRIVACY-PRESERVING INFORMATION RETRIEVAL

PRIVACY SENSITIVE ESTIMATION OF DIGITAL RESOURCE ACCESS FREQUENCY

Adaptive Privacy Budgeting and Adaptive Sampling Value Prediction

Training Machine-Learned Models with Label Differential Privacy

Distributed, Private, Sparse Histograms in the Two-Server Model