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Differentially Private Verification of Distribution Properties

arXiv 2604.10819 ↗First posted 12 Apr 2026cs.DS cs.CC cs.LGcited by 0

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Abstract (arXiv)

A recent line of work initiated by Chiesa and Gur and further developed by Herman and Rothblum investigates the sample and communication complexity of verifying properties of distributions with the assistance of a powerful, knowledgeable, but untrusted prover. In this work, we initiate the study of differentially private distribution property verification. After all, if we do not trust the prover to help us with verification, why should we trust it with our sensitive sample? We map a landscape of differentially private verification of properties of distributions. In the non-private case it is known that one-round private-coin protocols can have substantially lower complexity than public-coin (AM) protocols. In contrast, the possibility for improvement in differentially private interactive proofs depends on the privacy parameter regime and model. Drawing on connections between privacy and replicability and privacy amplification techniques in the literature we show: 1. There exists a reduction from any one-round (varepsilon,δ)-differentially private private-coin protocol to a differentially private AM protocol for the parameter regime varepsilon = O(1/√s) and δ= O(1/s5/2) with the same privacy and sample and communication complexities. In the local model, this is relaxed to varepsilon = O(1/√log s) 2. However, when the privacy guarantee is very relaxed (varepsilon ∈ Omega(log s)), private coins indeed reduce sample and communication complexities. We also obtain a computationally efficient Merlin-Arthur proof for privately testing whether samples are drawn from a product distribution and prove that its sample complexity is optimal up to a polylog N factor by reducing uniformity testing to independence testing with Boolean attributes and appealing to known lower bounds on sample complexity for private uniformity testing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 60 references that are in the map. First posted on arXiv 12 Apr 2026.

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19 of 60 references.

References · 19

  1. 2020Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shufflingcited by 96
  2. 2015Generalization in Adaptive Data Analysis and Holdout Reusecited by 83
  3. 2022Reproducibility in Learningcited by 44
  4. 2023Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalizationcited by 42
  5. 2015Optimal Testing for Properties of Distributionscited by 36
  6. 2016Max-Information, Differential Privacy, and Post-Selection Hypothesis Testingcited by 27
  7. 2017Differentially Private Testing of Identity and Closeness of Discrete Distributionscited by 25
  8. 2017Optimal Identity Testing with High Probabilitycited by 19
  9. 2014Testing probability distributions underlying aggregated datacited by 15
  10. 2016Collision-based Testers are Optimal for Uniformity and Closenesscited by 15
  11. 2019Private Identity Testing for High-Dimensional Distributionscited by 15
  12. 2021The Price of Tolerance in Distribution Testingcited by 14
7 more
  1. 2018Test without Trust: Optimal Locally Private Distribution Testingcited by 13
  2. 2022Private High-Dimensional Hypothesis Testingcited by 12
  3. 2017Generalized Uniformity Testingcited by 3
  4. 2024How to Verify Any (Reasonable) Distribution Property: Computationally Sound Argument Systems for Distributionscited by 3
  5. 2017Differentially Private Identity and Closeness Testing of Discrete Distributionscited by 2
  6. 2025Better Private Distribution Testing by Leveraging Unverified Auxiliary Datacited by 2
  7. 2026Optimal Prediction-Augmented Algorithms for Testing Independence of Distributionscited by 1

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