# Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy

@article{Covington2021UnbiasedSE, title={Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy}, author={Christian Covington and Xi He and James Honaker and Gautam Kamath}, journal={ArXiv}, year={2021}, volume={abs/2110.14465} }

We present a method for producing unbiased parameter estimates and valid confidence intervals under the constraints of differential privacy, a formal framework for limiting individual information leakage from sensitive data. Prior work in this area is limited in that it is tailored to calculating confidence intervals for specific statistical procedures, such as mean estimation or simple linear regression. While other recent work can produce confidence intervals for more general sets of…

## 3 Citations

Privacy Preserving Inference on the Ratio of Two Gaussians Using (Weighted) Sums

- Computer Science, MathematicsArXiv
- 2021

The delta method is used to derive the asymptotic distribution of the ratio estimator and the Gaussian mechanism to provide (ǫ, δ) privacy guarantees and it is shown that the CIs of the methods have the right coverage with proper privacy budget.

Differentially private inference via noisy optimization

- Computer Science, MathematicsArXiv
- 2021

This work shows that robust statistics can be used in conjunction with noisy gradient descent or noisy Newton methods in order to obtain optimal private estimators with global linear or quadratic convergence, respectively, and establishes local and global convergence guarantees.

Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment

- Computer ScienceProceedings of the 2021 on Cloud Computing Security Workshop
- 2021

This paper outlines a way to deploy an end-to-end privacy-preserving protocol for learning causal effects from Randomized Controlled Trials, particularly focused on the difficult and important case where one party determines which treatment an individual receives, and another party measures outcomes on individuals, and these parties do not want to leak any of their information to each other.

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