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ProxyGuard Infers Reliability of Randomized Data Release Mechanisms

Dipesh Tharu Mahato, Pramod Dhungana· August 20, 2026 View original

Key takeaways

  • ProxyGuard rigorously evaluates randomized data release mechanism reliability.
  • It controls for multiplicity and ensures valid releases with bounded risks.
  • "Direct shared-target mode" significantly boosts evaluation power.
  • Provides finite-sample reliability guarantees without independent target batches.

Who benefits

Data ScienceCybersecurityFinancial ServicesHealthcareGovernment

Summary

ProxyGuard is a new method for directly inferring the reliability of randomized data release mechanisms, controlling for multiplicity and ensuring valid releases. It provides finite-sample guarantees without independent target batches, improving power for evaluating mechanisms that generate multiple datasets.

Researchers often face the challenge of selecting a reliable dataset from multiple randomized releases, transformations, or seeds. Traditional search methods can mistakenly validate an inadequate release, and a single adequate release doesn't guarantee the generator's overall reliability. ProxyGuard is introduced to address these issues by controlling for both types of errors using prespecified bounded risks and a sealed target set. The method operates in two modes: "Named-release mode" corrects for multiplicity and certifies specific releases, while "Direct shared-target mode" evaluates independent mechanism draws on a common target. This direct mode lower-bounds the rate of favorable scores and subtracts a bound on favorable scores from invalid releases. Crucially, it provides finite-sample mechanism-reliability guarantees without requiring independent target batches or assumptions on p-value dependence. Experiments show that direct mode significantly boosts power (from 5.6% to 64.2% at 0.95 reliability), making it a powerful tool for auditing data release mechanisms, including complex pipelines like Rice--TVAE and non-tabular text mechanisms.

Why it matters

Professionals in data science, privacy, and compliance can use ProxyGuard to rigorously evaluate and certify the reliability of randomized data release mechanisms, ensuring data quality and mitigating risks associated with data generation.

How to implement this in your domain

  1. 1Adopt ProxyGuard to audit and certify the reliability of data generation and release mechanisms within your organization.
  2. 2Integrate ProxyGuard's "Named-release mode" to validate specific data releases, especially for sensitive or critical applications.
  3. 3Utilize the "Direct shared-target mode" to assess the overall reliability of data generation pipelines that produce multiple outputs.
  4. 4Train data governance and privacy teams on the principles and application of ProxyGuard for enhanced data quality assurance.

Original post by Dipesh Tharu Mahato, Pramod Dhungana

"arXiv:2608.18643v1 Announce Type: new Abstract: Researchers often choose a proxy dataset from many releases, transformations, or seeds. Search can make an invalid release appear adequate, while one adequate release does not establish that its generator is reliable. ProxyGuard con…"

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Originally posted by Dipesh Tharu Mahato, Pramod Dhungana on X · view source

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