privacy
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Black-Box Differential Privacy for Interactive ML
We show that any (possibly non-private) learning rule can be effectively transformed to a private learning rule with only a polynomial overhead in the mistake bound.
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- Asia > Middle East > Israel > Tel Aviv District > Tel Aviv (0.04)
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- Education > Educational Setting > Online (0.47)
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Differentially Private Statistical Inference through β-Divergence One Posterior Sampling Jack Jewson
Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process.
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- Europe > United Kingdom > England > Oxfordshire > Oxford (0.14)
- North America > United States > New Jersey (0.04)
- Europe > Spain > Catalonia > Barcelona Province > Barcelona (0.04)
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- Information Technology > Security & Privacy (1.00)
- Health & Medicine (1.00)
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Breaking the Communication-Privacy-Accuracy Tradeoff with f-Differential Privacy
On one hand, the local data of users may contain sensitive information, and privacy-preserving mechanisms are needed.
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- Asia > China (0.04)
- North America > United States > North Carolina (0.04)
- Asia > Singapore (0.04)
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- Europe > Germany > Baden-Württemberg > Tübingen Region > Tübingen (0.14)
- Europe > Germany > Bavaria > Upper Bavaria > Munich (0.04)
- North America > United States (0.04)
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Dynamic Personalized Federated Learning with Adaptive Differential Privacy Xiyuan Y ang 1 Wenke Huang 1 Mang Y e
Despite its pilot progress, personalized federated learning with differential privacy still faces numerous challenges.
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- North America > United States > Virginia (0.05)
- Asia > China > Hubei Province > Wuhan (0.04)
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Faster approximate subgraph counts with privacy
These counts have very high global sensitivity.
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- North America > United States > Oregon (0.04)
- North America > United States > Virginia (0.04)
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- Africa > Senegal > Kolda Region > Kolda (0.04)
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