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- Year in Review
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November 25, 2024
Bridging the gap in differentially private model training- Algorithms & Theory ·
- Distributed Systems & Parallel Computing ·
- Machine Intelligence ·
- Security, Privacy and Abuse Prevention
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May 16, 2024
Protecting users with differentially private synthetic training data- Machine Intelligence ·
- Natural Language Processing ·
- Security, Privacy and Abuse Prevention
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April 19, 2024
Improving Gboard language models via private federated analytics- Algorithms & Theory ·
- Distributed Systems & Parallel Computing ·
- Product ·
- Security, Privacy and Abuse Prevention
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February 21, 2024
Advances in private training for production on-device language models- Mobile Systems ·
- Product ·
- Responsible AI ·
- Security, Privacy and Abuse Prevention
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February 13, 2024
DP-Auditorium: A flexible library for auditing differential privacy- Responsible AI ·
- Security, Privacy and Abuse Prevention
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December 8, 2023
Sparsity-preserving differentially private training- Algorithms & Theory ·
- Machine Intelligence ·
- Security, Privacy and Abuse Prevention
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December 4, 2023
Summary report optimization in the Privacy Sandbox Attribution Reporting API- Algorithms & Theory ·
- Economics & Electronic Commerce ·
- Security, Privacy and Abuse Prevention
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September 8, 2023
Differentially private median and more- Security, Privacy and Abuse Prevention
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June 29, 2023
Announcing the first Machine Unlearning Challenge- Machine Intelligence ·
- Security, Privacy and Abuse Prevention
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May 25, 2023
Differentially private clustering for large-scale datasets- Machine Intelligence ·
- Responsible AI ·
- Security, Privacy and Abuse Prevention
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May 19, 2023
Making ML models differentially private: Best practices and open challenges- Machine Intelligence ·
- Security, Privacy and Abuse Prevention
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April 18, 2023
Differentially private heatmaps- Algorithms & Theory ·
- Machine Intelligence ·
- Security, Privacy and Abuse Prevention