Private independence testing across two parties

Alex Pentland; Cl\'ement L. Canonne; Mohammad Mohammadi Amiri; Praneeth Vepakomma; Ramesh Raskar

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Private independence testing across two parties

Praneeth Vepakomma, Mohammad Mohammadi Amiri, Clément L. Canonne, Ramesh Raskar, Alex Pentland

Statistics Theory · Submitted 8 Jul 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

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arXiv ID2207.03652 (version 2)

Pith IDE7D5DMCA, Pith's permanent record of this paper and its review

CitationAlex Pentland, Cl\'ement L. Canonne, Mohammad Mohammadi Amiri, Praneeth Vepakomma, Ramesh Raskar. "Private independence testing across two parties." arXiv:2207.03652 (2022). https://pith.science/paper/E7D5DMCA

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We introduce $\pi$-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.

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#	sentence	role	the authors' own work	states the main conclusion	strength
s0	We introduce $\pi$-test, a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties.	contribution 1.00	0.93	0.35	asserted 1.00
s1	Our algorithm relies on privately estimating the distance correlation between datasets, a quantitative measure of independence introduced in Sz\'ekely et al. [2007].	method 0.94	0.14	0.08	asserted 1.00
s2	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.	result 0.64	0.91	0.48	asserted 0.74

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Original abstract

We introduce $\pi$-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.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Furthermore, Gaboardi et al. [ref] use local differential privacy to secure the $χ^2$-test, and Vepakomma et al. [ref] propose a differentially private independence testing across two parties.

Towards Practical Federated Causal Structure Learning · Related Work · 0.99

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Cites as background 2 papers

The works in [ref] performed conditional independence testing and applied them to the problem of causal discovery.

Kernel-based Conditional Independence Test and Application in Causal Discovery · Related Work · 0.98
That said, universality results on power analysis of distance covariance based tests are being formulated by various groups in recent preprints such as [ref] .

Generalized kernel distance covariance in high dimensions: non-null CLTs and power universality · Limitations and future work · 0.97

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