Privacy-Accuracy Trade-offs in High-Dimensional LASSO under Perturbation Mechanisms

Privacy-Accuracy Trade-offs in High-Dimensional LASSO under Perturbation Mechanisms | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/privacy-accuracy-trade-offs-in-high-dimensional-lasso-under-perturbation-mechanisms

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Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 1
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID privacy-accuracy-trade-offs-in-high-dimensional-lasso-under-perturbation-mechanisms | Route /signal-canvas/privacy-accuracy-trade-offs-in-high-dimensional-lasso-under-perturbation-mechanisms

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source_context

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Evidence Receipt

Route status: building

Claims: 7

References: 1

Proof: Verification pending

Freshness state: computing

Source paper: Privacy-Accuracy Trade-offs in High-Dimensional LASSO under Perturbation Mechanisms

PDF: https://arxiv.org/pdf/2603.26227v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T23:13:23.404Z

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Not build-ready: Privacy-Accuracy Trade-offs in High-Dimensional LASSO under Perturbation Mechanisms

/buildability/privacy-accuracy-trade-offs-in-high-dimensional-lasso-under-perturbation-mechanisms

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Subject: Privacy-Accuracy Trade-offs in High-Dimensional LASSO under Perturbation Mechanisms

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
Using approximate message passing (AMP), we characterize the typical behavior of these estimators under random design and privacy noise.
Implicationpartial
The abstract explicitly states the use of AMP for analysis.
Verificationpartial
partial
Evidencepartial
Our analysis reveals that sparsity plays a central role in shaping the privacy-accuracy trade-off: stronger regularization can improve privacy by stabilizing the estimator against single-point data changes.
Implicationpartial
The abstract directly states this finding and its implication for regularization.
Verificationpartial
partial
Evidencepartial
We further show that the two mechanisms exhibit qualitatively different behaviors.
Implicationpartial
The abstract explicitly highlights the differing behaviors of the two mechanisms.
Verificationpartial
partial
Evidencepartial
In particular, for objective perturbation, increasing the noise level can have non-monotonic effects, and excessive noise may destabilize the estimator, leading to increased sensitivity to data perturbations.
Implicationpartial
The abstract details specific non-monotonic effects and potential destabilization with increased noise for objective perturbation.
Verificationpartial
partial
Evidencepartial
To quantify privacy, we adopt typical-case measures, including the on-average KL divergence, which admits a hypothesis-testing interpretation in terms of distinguishability between neighboring datasets.
Implicationpartial
The abstract mentions the use of typical-case measures including on-average KL divergence.
Verificationpartial
partial
Evidencepartial
If the privacy noise satisfies Assumption P, then in the high-dimensional limit n, p -> infinity with alpha=n/p=O(1), the estimator is asymptotically equivalent in distribution to
Implicationpartial
Theorem 4.1 and subsequent equations describe this asymptotic equivalence.
Verificationpartial
partial
Evidencepartial
Claim 5.1 (Equivalent distribution induced by objective privacy noise)For a fixed datasetD, the distribution of a component of the AMP estimator over the privacy noise is asymptotically equivalent to the scalar distribution generated by the state evolution with Gaussian noisez
Implicationpartial
Claim 5.1 directly states this equivalence.
Verificationpartial
partial

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Privacy-Accuracy Trade-offs in High-Dimensional LASSO under Perturbation Mechanisms

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