mlsanityData to 7 Oct 2026

Private independence testing across two parties

arXiv 2207.03652 ↗First posted 8 Jul 2022math.ST cs.CR cs.LGcited by 2

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Abstract (arXiv)

We introduce π-test, a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties. Our algorithm relies on privately estimating the distance correlation between datasets, a quantitative measure of independence introduced in Sz'ekely et al. [2007]. We establish both additive and multiplicative error bounds on the utility of our differentially private test, which we believe will find applications in a variety of distributed hypothesis testing settings involving sensitive data.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 8 Jul 2022.

Builds on

14 of 33 references.

References · 14

  1. 2020CoinPress: Practical Private Mean and Covariance Estimationcited by 74
  2. 2017A Linear-Time Kernel Goodness-of-Fit Testcited by 41
  3. 2016Large-Scale Kernel Methods for Independence Testingcited by 32
  4. 2017Differentially Private Testing of Identity and Closeness of Discrete Distributionscited by 25
  5. 2016Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testingcited by 24
  6. 2016An Adaptive Test of Independence with Analytic Kernel Embeddingscited by 13
  7. 2021Inference under Information Constraints III: Local Privacy Constraintscited by 13
  8. 2018Locally Private Hypothesis Testingcited by 11
  9. 2020Towards practical differentially private causal graph discoverycited by 10
  10. 2019The Chi-Square Test of Distance Correlationcited by 8
  11. 2016Supervised Dimensionality Reduction via Distance Correlation Maximizationcited by 7
  12. 2014Consistent distribution-free K-sample and independence tests for univariate random variablescited by 5
2 more
  1. 2016Testing mutual independence in high dimension via distance covariancecited by 4
  2. 2019Asymptotic Distributions of High-Dimensional Distance Correlation Inferencecited by 3

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Towards Practical Federated Causal Structure Learningcited by 9
  2. 2023Differentially Private Conditional Independence Testingcited by 0