Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling

• URL: http://arxiv.org/abs/2301.08324v2
• Date: Thu, 11 Apr 2024 16:37:33 GMT
• Title: Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling
• Authors: Shurong Lin, Mark Bun, Marco Gaboardi, Eric D. Kolaczyk, Adam Smith,
• Abstract summary: With the increase of data privacy awareness, developing a private version of confidence intervals has gained growing attention. Recent work has been done around differentially private confidence intervals, yet rigorous methodologies on differentially private confidence intervals have not been studied. We propose three differentially private algorithms for constructing confidence intervals for proportions under stratified random sampling.
• Score: 14.066813980992132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Confidence intervals are a fundamental tool for quantifying the uncertainty of parameters of interest. With the increase of data privacy awareness, developing a private version of confidence intervals has gained growing attention from both statisticians and computer scientists. Differential privacy is a state-of-the-art framework for analyzing privacy loss when releasing statistics computed from sensitive data. Recent work has been done around differentially private confidence intervals, yet to the best of our knowledge, rigorous methodologies on differentially private confidence intervals in the context of survey sampling have not been studied. In this paper, we propose three differentially private algorithms for constructing confidence intervals for proportions under stratified random sampling. We articulate two variants of differential privacy that make sense for data from stratified sampling designs, analyzing each of our algorithms within one of these two variants. We establish analytical privacy guarantees and asymptotic properties of the estimators. In addition, we conduct simulation studies to evaluate the proposed private confidence intervals, and two applications to the 1940 Census data are provided.

Related papers

• Differentially Private Covariate Balancing Causal Inference [8.133739801185271]
  Differential privacy is the leading mathematical framework for privacy protection. Our algorithm produces both point and interval estimators with statistical guarantees, such as consistency and rate optimality, under a given privacy budget.
  arXiv  Detail & Related papers  (2024-10-18T18:02:13Z)
• TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data [50.797729676285876]
  We propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously. We theoretically quantify the privacy guarantee through the lens of the emerging f-differential privacy (DP) and the Byzantine resilience of the proposed algorithm.
  arXiv  Detail & Related papers  (2024-02-16T16:41:14Z)
• Federated Experiment Design under Distributed Differential Privacy [31.06808163362162]
  We focus on the rigorous protection of users' privacy while minimizing the trust toward service providers. Although a vital component in modern A/B testing, private distributed experimentation has not previously been studied. We show how these mechanisms can be scaled up to handle the very large number of participants commonly found in practice.
  arXiv  Detail & Related papers  (2023-11-07T22:38:56Z)
• Differentially Private Distributed Estimation and Learning [2.4401219403555814]
  We study distributed estimation and learning problems in a networked environment. Agents exchange information to estimate unknown statistical properties of random variables from privately observed samples. Agents can estimate the unknown quantities by exchanging information about their private observations, but they also face privacy risks.
  arXiv  Detail & Related papers  (2023-06-28T01:41:30Z)
• Private Domain Adaptation from a Public Source [48.83724068578305]
  We design differentially private discrepancy-based algorithms for adaptation from a source domain with public labeled data to a target domain with unlabeled private data. Our solutions are based on private variants of Frank-Wolfe and Mirror-Descent algorithms.
  arXiv  Detail & Related papers  (2022-08-12T06:52:55Z)
• Private independence testing across two parties [21.236868468146348]
  $pi$-test is a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties. We establish both additive and multiplicative error bounds on the utility of our differentially private test.
  arXiv  Detail & Related papers  (2022-07-08T02:13:05Z)
• Debugging Differential Privacy: A Case Study for Privacy Auditing [60.87570714269048]
  We show that auditing can also be used to find flaws in (purportedly) differentially private schemes. In this case study, we audit a recent open source implementation of a differentially private deep learning algorithm and find, with 99.99999999% confidence, that the implementation does not satisfy the claimed differential privacy guarantee.
  arXiv  Detail & Related papers  (2022-02-24T17:31:08Z)
• Post-processing of Differentially Private Data: A Fairness Perspective [53.29035917495491]
  This paper shows that post-processing causes disparate impacts on individuals or groups. It analyzes two critical settings: the release of differentially private datasets and the use of such private datasets for downstream decisions. It proposes a novel post-processing mechanism that is (approximately) optimal under different fairness metrics.
  arXiv  Detail & Related papers  (2022-01-24T02:45:03Z)
• Non-parametric Differentially Private Confidence Intervals for the Median [3.205141100055992]
  This paper proposes and evaluates several strategies to compute valid differentially private confidence intervals for the median. We also illustrate that addressing both sources of uncertainty--the error from sampling and the error from protecting the output--should be preferred over simpler approaches that incorporate the uncertainty in a sequential fashion.
  arXiv  Detail & Related papers  (2021-06-18T19:45:37Z)
• Robustness Threats of Differential Privacy [70.818129585404]
  We experimentally demonstrate that networks, trained with differential privacy, in some settings might be even more vulnerable in comparison to non-private versions. We study how the main ingredients of differentially private neural networks training, such as gradient clipping and noise addition, affect the robustness of the model.
  arXiv  Detail & Related papers  (2020-12-14T18:59:24Z)
• CoinDICE: Off-Policy Confidence Interval Estimation [107.86876722777535]
  We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning. We show in a variety of benchmarks that the confidence interval estimates are tighter and more accurate than existing methods.
  arXiv  Detail & Related papers  (2020-10-22T12:39:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.