Research map / Federated learning and privacy / Differential privacy
Locally Private Bayesian Inference for Count Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model the unprivatized counts as latent variables and infer them jointly with model parameters, rather than treating privatized counts as true data | Poisson factorization on Enron email data · Enron topic modeling | reconstruction MAE | Helped |
| Model the unprivatized counts as latent variables and infer them jointly with model parameters, rather than treating privatized counts as true data | Poisson factorization on Enron email data · Enron topic modeling | NPMI | Helped |
| Model the unprivatized counts as latent variables and infer them jointly with model parameters, rather than treating privatized counts as true data | Poisson factorization on Enron email data · Enron topic modeling | topic coherence | Helped |
| Model the unprivatized counts as latent variables and infer them jointly with model parameters, rather than treating privatized counts as true data | Poisson factorization on Enron email data · Enron social network reconstruction | reconstruction MAE | Helped |
| Model the unprivatized counts as latent variables and infer them jointly with model parameters, rather than treating privatized counts as true data | Poisson factorization on Enron email data · Enron heldout link prediction | heldout MAE | Helped |
| Model the unprivatized counts as latent variables and infer them jointly with model parameters, rather than treating privatized counts as true data | Poisson factorization on synthetic data · Synthetic topic modeling | ground-truth topic recovery | Helped |
| Model the unprivatized counts as latent variables and infer them jointly with model parameters, rather than treating privatized counts as true data | Poisson factorization on synthetic data · Synthetic social network | block structure recovery | Helped |
Closest papers by meaning
- On the Differential Privacy of Bayesian Inference (AAAI 2016 · similarity 0.90)
- DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference (ICML 2023 · similarity 0.89)
- Locally Optimal Private Sampling: Beyond the Global Minimax (NeurIPS 2025 · similarity 0.89)
- Privacy Guarantees in Posterior Sampling under Contamination (NeurIPS 2026 · similarity 0.88)
- On Inherent Privacy of Posterior Sampling: A Unified R\'enyi-Divergence Framework (NeurIPS 2026 · similarity 0.87)
- Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy (ICLR 2024 · similarity 0.87)
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