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Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read LLM-generated surrogate public data—synthesized from schema metadata alone—can effectively replace traditional public data when pretraining differentially private tabular classifiers, with the strongest gains on small datasets.

desk verdict A broad and genuinely useful empirical study of LLM-generated surrogate public data for DP tabular ML; the main pretraining result holds on small datasets, but the independence-from-private-data assumption needs stronger support. read the letter →

arxiv 2504.14368 v1 pith:HP4WIQ5Y submitted 2025-04-19 cs.LG cs.CR

Shlomi Hod , Lucas Rosenblatt , Julia Stoyanovich This is my paper · ORCID
classification cs.LGcs.CR
keywords differentialprivacysurrogatepublicdatatabularlargelanguagemodelsDPpretrainingstructuralcausalhyperparametertuningprivacy-utilitytradeoff
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Differentially private tabular learning usually assumes a public dataset exists for pretraining, tuning, and estimating the privacy-utility trade-off, but such public samples are often unavailable for tabular data. This paper introduces surrogate public data: datasets generated purely from schema-level metadata, consuming no privacy budget, and proposes automating that generation with large language models. The central claim is that these surrogates can effectively replace traditional public data when pretraining DP tabular classifiers, with the strongest evidence in the low-data regime: at $\varepsilon=1$ the traditional public split on EDAD gives an AUC advantage of 0.19 while Claude and Llama CSV surrogates give 0.17, and on WE a Claude Agent surrogate gives 0.21 versus 0.11 for the public split. Surrogates also help, to a lesser extent, for hyperparameter tuning of private synthetic-data generators and for estimating the privacy-utility trade-off.

What carries the argument

The central object is surrogate public data plus a schema-to-data generation pipeline. The Agent method is the load-bearing piece: an automated state machine that prompts an LLM to declare variables, propose consistency constraints, choose root nodes, add parent-child edges while checking acyclicity, assign structural equations and parameters, emit sampling code, and enforce value ranges, re-querying on any validation failure. This encodes plausible dependencies among variables using only schema metadata, making the sampled records statistically coherent while consuming zero privacy budget. Direct CSV generation is the simpler alternative, and the uniform, univariate, and arbitrary Bayesian-network baselines isolate which data properties actually matter.

What would settle it

Take a private split that was published before the generation models' training cutoffs and run the paper's own header-generation and row-completion memorization tests on the LLMs used to build surrogates; if the LLMs reproduce exact or near-exact private records, the reported pretraining gains cannot be attributed to schema-based priors.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that LLMs can stand in for the panel of domain experts that would manually encode a plausible data-generating process, turning public schema or codebook metadata into usable public data. It proposes two automated routes: direct CSV record generation, and a state-machine Agent that elicits a structural causal model (a DAG with structural equations) from the LLM and then samples from it. Across three datasets, the paper finds that LLM-generated surrogates match traditional public data when pretraining a DP FTTransformer under DP-SGD fine-tuning at $\varepsilon=1$ on small datasets, with the strongest aggregate results for direct CSV generation from Claude on EDAD and for the Claude Agent on the Workplace Equity survey. On the large ACS dataset, no pretraining method helps, and a subsampling analysis shows the advantage reappears at 5% and 10% sizes, which the paper attributes to the inverse relationship between noise and dataset size.

Load-bearing premise

The load-bearing premise is that the large language models used to generate the surrogate data never memorized the private evaluation records, so the measured gains reflect the schema-based prior rather than data leakage.

Editorial extensions

If this is right

  • Practitioners with a small private tabular dataset and no public split can pretrain a DP classifier on LLM-generated surrogate data and keep most of the pretraining benefit that a real public sample would provide.
  • The input that matters is schema metadata, so a codebook or README—rather than an actual public sample—can be sufficient to build a zero-privacy-cost prior for private learning.
  • Surrogate pretraining is a low-data-regime tool: as the private dataset grows, the advantage disappears, so large-scale deployments should not expect gains from this technique.
  • For tuning DP synthesizers, encoding any plausible dependency structure can be enough to choose useful hyperparameters, which explains why an arbitrary Bayesian network lands on the Pareto frontier alongside the LLM-generated surrogates.
  • Estimating the privacy-utility trade-off is the hardest of the three tasks; surrogate data tracks the private curve only loosely, and traditional public data remains the best estimator in most settings.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: If schema priors are what carry the gains, then the binding constraint becomes metadata quality, and augmenting sparse codebooks with retrieval from domain literature or expert review of the elicited SCM should push surrogates closer to parity with real public splits.
  • Editorial inference: The large-dataset failure implies a measurable size threshold—somewhere between the 1,400-record WE split and the 23,006-record ACS split at $\varepsilon=1$—where surrogate pretraining stops beating no pretraining; locating that threshold across datasets would give practitioners a rule of thumb.
  • Editorial inference: The absence of a link between TVD and 3-way marginal similarity and usefulness suggests the right similarity measure for surrogate selection is task-relative, such as fidelity of the conditional distribution of the target given features, and such a metric could select among generated surrogates without spending privacy budget.
  • Editorial inference: Because CSV and Agent surrogates succeed in different spots (CSV on EDAD pretraining, Agent on WE pretraining and tuning), a portfolio that generates both and picks by task may beat either alone; the paper does not test this.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper introduces the notion of "surrogate public data" for differentially private (DP) tabular analytics: datasets generated from schema-level metadata only, without access to sensitive records and without consuming privacy budget. Two LLM-based generation methods are proposed: direct CSV generation and an agent-based state machine that elicits a structural causal model (SCM) from an LLM. These are compared against simpler baselines (uniform, univariate, arbitrary Bayesian network) and against traditional public data on three datasets (ACS, EDAD, WE), three DP synthesizers (PrivBayes, GEM, AIM), and an FTTransformer classifier. The main empirical finding is that surrogate public data can substantially improve DP classifier pretraining in the low-data regime, with EDAD and WE showing large AUC advantages at small epsilon, while ACS at full size shows no pretraining benefit. The paper also reports more limited usefulness for hyperparameter tuning of DP synthesizers and a mixed story for privacy-utility tradeoff estimation.

Significance. If the central claim holds, the paper makes a practically valuable contribution: it relaxes the common assumption that DP tabular pipelines require access to traditional public data, and it offers a concrete, automatable way to generate domain-informed priors from public schema information. The experimental design is broad and reproducible, with public code, multiple LLMs, multiple synthesizers, multiple epsilon values, and repeated runs. The authors also take the memorization issue seriously by choosing post-cutoff private splits and applying the Bordt et al. row-level memorization tests. However, the validity of the central claim depends on a load-bearing independence assumption: the LLM-generated surrogate data must not encode information from the private splits. The paper's evidence for this assumption is incomplete, and the headline quantitative comparisons lack uncertainty quantification. These issues do not overturn the empirical phenomenon but do affect how strongly the central claim can be stated.

major comments (3)
  1. [8.1, Appendix B.1.4] The paper's non-leakage evidence is insufficient for the load-bearing independence requirement. The Bordt et al. (2024) tests used in Appendix B.1.4 target verbatim record-level memorization (header tests and row completion tests); they do not test whether an LLM has internalized published aggregate statistics of the private EDAD 2023 and WE 2023 releases, such as marginal distributions, correlation tables, or descriptive summaries in codebooks and press releases. This matters because EDAD 2023 and WE 2023 were released in April 2024, overlapping with the stated cutoff of Claude 3.5 Sonnet (April 2024) and postdating the surveys themselves, so exposure to public summaries of these very releases is plausible. The paper explicitly acknowledges this risk in Section 8.1 but then treats the row-level memorization test as sufficient. It is not sufficient for distributional leakage. To support the central claim that the observed gains come from schema-based priors rather than private-informative prior contamination, the authors should add tests that compare generated surrogate data against publicly available aggregate summaries, or otherwise demonstrate that the LLM's output is insensitive to the private distribution's published summary statistics.
  2. [6.1, Table 3] The headline pretraining results are presented as point estimates without standard errors, confidence intervals, or significance tests, although 10 runs per configuration are reported. For example, on EDAD at epsilon=1 the AUC advantage is 0.19 for traditional public data, 0.17 for CSV (Claude), and 0.17 for CSV (Llama); the differences among these methods are small relative to the expected variance of DP training. Similarly, the WE results at epsilon=1 show Agent (Claude, Unif.) at 0.21 versus Public at 0.11, but no measure of variability is given. The claim that surrogate methods are "on par with" traditional public data, and the ranking of methods in Figures 3-9, would be substantially strengthened by reporting standard errors or confidence intervals and, where appropriate, paired significance tests across the shared fine-tuning hyperparameter configurations.
  3. [6.1, Figure 9] The explanation that the ACS failure is due to dataset size rests on subsampling a single dataset (ACS) at four rates. This is suggestive but not conclusive: the observed difference between full-size ACS and the small EDAD/WE datasets could also be explained by domain, schema complexity, or the strength of the LLM's prior for the specific variables. Since the low-data regime is the core of the paper's central claim, the evidence base of two small datasets is thin. The authors should either add at least one additional large dataset (or an additional small one from a different domain) to test the size hypothesis, or explicitly scope the claim to the two demonstrated datasets and temper the abstract accordingly.
minor comments (4)
  1. [Section 3 heading] The section title "statical distance metrics" contains a typo and should read "statistical distance metrics."
  2. [4.1.2, footnote 2] The Univariate baseline is explicitly disclosed as not a valid public data surrogate, but this disclosure appears only in a footnote. Because the method appears as a Pareto efficient method in Task 3 results (e.g., Table 14), the main text should clearly mark it as a reference condition and not as a candidate surrogate.
  3. [Table 16, Appendix D] In Table 16, zero values are omitted for readability, but this makes it difficult to distinguish true zero similarity from missing entries, particularly for EDAD and WE where many LLM-based rows contain only blanks. A placeholder such as "<0.05" or explicit zeros would improve interpretability.
  4. [Appendix C.1, Table 22a] The table contains a typo: "Agent (Allm Unif.)" should be "Agent (All, Unif.)."

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is an empirical benchmark, and the surrogate generation methods do not access sensitive records.

full rationale

The paper's central claim — that LLM-generated surrogate public data can effectively replace traditional public data in DP auxiliary tasks — is supported by a controlled empirical evaluation, not by a derivation that reduces to its own inputs. Surrogate generation (CSV and Agent) consumes only schema-level metadata, and the evaluation includes both a no-pretraining control and a traditional public split as external points of comparison. Reported AUC advantages are measured on a held-out private test split after fine-tuning, so they are not fitted quantities renamed as predictions. The only baseline that samples directly from private data (Univariate) is explicitly disclosed as not a valid surrogate in Section 4.1.2, so it cannot serve as a hidden fitted input. Self-citations, such as Hod & Canetti (2025) for motivation and Rosenblatt et al. (2024b) for FTTransformer adaptation, are not load-bearing for the central empirical claim. The acknowledged memorization risk in Section 8.1 is a validity threat about whether the Bordt et al. row-level tests rule out distributional leakage, but it is not a circularity: no equation or fitted quantity in the paper makes the conclusion equivalent to its inputs. The paper is self-contained against external benchmarks and control conditions, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No free parameters were fitted to sensitive data; the claims rest on empirical evaluation and the domain assumptions above. No new physical entities are introduced.

assumptions (5)
  • standard math Differential privacy definitions and composition (Dwork et al., 2016).
    Used as background in Section 3.1 and Definition 1.
  • standard math Public data close to the private distribution improves private learning (Bassily et al., 2019).
    Motivates the surrogate approach in Section 1.
  • domain assumption LLM pretrained knowledge encodes plausible relationships among tabular variables from schema descriptions.
    The paper's generation methods rely on this; it is supported empirically but not proven.
  • domain assumption Private splits of EDAD (2023) and WE (2023) postdate LLM knowledge cutoffs and are not memorized.
    Appendix B.1.4 uses Bordt et al. (2024) tests to support this; if false, pretraining gains could reflect leakage.
  • domain assumption Schema-level metadata alone (names, types, ranges) is publicly available for tabular datasets.
    The surrogate generation requires only this input, per Section 4.

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Pith. "Pith review of Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data." pith.science (2026). https://pith.science/paper/HP4WIQ5Y

@misc{pith2026250414368,
  author       = {Pith},
  title        = {Pith review of: Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HP4WIQ5Y}},
  note         = {Machine review of arXiv:2504.14368}
}
read the original abstract

Differentially private (DP) machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretraining. While public data assumptions may be reasonable in text and image domains, they are less likely to hold for tabular data due to tabular data heterogeneity across domains. We propose leveraging powerful priors to address this limitation; specifically, we synthesize realistic tabular data directly from schema-level specifications - such as variable names, types, and permissible ranges - without ever accessing sensitive records. To that end, this work introduces the notion of "surrogate" public data - datasets generated independently of sensitive data, which consume no privacy loss budget and are constructed solely from publicly available schema or metadata. Surrogate public data are intended to encode plausible statistical assumptions (informed by publicly available information) into a dataset with many downstream uses in private mechanisms. We automate the process of generating surrogate public data with large language models (LLMs); in particular, we propose two methods: direct record generation as CSV files, and automated structural causal model (SCM) construction for sampling records. Through extensive experiments, we demonstrate that surrogate public tabular data can effectively replace traditional public data when pretraining differentially private tabular classifiers. To a lesser extent, surrogate public data are also useful for hyperparameter tuning of DP synthetic data generators, and for estimating the privacy-utility tradeoff.

Figures

Figures reproduced from arXiv: 2504.14368 by the authors.

Figure 1
Figure 1. An overview of the premise of this work: Can [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. An overview of our evaluation framework. We assess the usefulness of regular public data and [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Mean AUC on the test subset of the private dataset split for the pretraining model and the fine-tuned [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figures from the paper (18 more)
Figure 6
Figure 6. Figure 6: ACS [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 8
Figure 8. Figure 8: Mean AUC on the test subset of the private dataset split for the pretraining model and the fine-tuned [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Mean AUC Advantage of the DP model with ε = 1 after pretraining for each subsampled dataset, grouped by generation method category. The mean is calculated across the DP finetuning hyperparameter space when best pretraining hyperparameter configuration is chosen for the…
Figure 10
Figure 10. Figure 10: Excerpt from the schema of the EDAD dataset (Spanish disability, autonomy, and dependency [PITH_FULL_IMAGE:figures/full_fig_p033_10.png]
Figure 11
Figure 11. Figure 11: The prompt template used for CSV generation with an LLM. [PITH_FULL_IMAGE:figures/full_fig_p034_11.png]
Figure 12
Figure 12. Figure 12: State machine for the SCM Agent showing state transitions. Each state can transition to itself upon failure or advance to the next state upon success, following a zigzag pattern. 35 [PITH_FULL_IMAGE:figures/full_fig_p035_12.png]
Figure 13
Figure 13. Figure 13: The header test output on the ACS dataset on Claude 3.5 Sonnet. The LLM is prompted with the [PITH_FULL_IMAGE:figures/full_fig_p038_13.png]
Figure 14
Figure 14. Figure 14: Mean AUC Advantage of the DP model after pretraining, grouped by generation method. The [PITH_FULL_IMAGE:figures/full_fig_p041_14.png]
Figure 15
Figure 15. Figure 15: Mean AUC Advantage of the DP model after pretraining, grouped by generation method for the [PITH_FULL_IMAGE:figures/full_fig_p042_15.png]
Figure 25
Figure 25. Figure 25: Privacy/utility tradeoff estimation results in terms of [PITH_FULL_IMAGE:figures/full_fig_p047_25.png]
Figure 26
Figure 26. Figure 26: Privacy/utility tradeoff estimation results in terms of [PITH_FULL_IMAGE:figures/full_fig_p048_26.png]
Figure 27
Figure 27. Figure 27: To provide intuition for exactly what the ℓ1 and ℓ2 scores in Figures 25 and 26 attempt to capture, we plot the average performance across epsilon that constitutes each vector, relative to the true performance of the sensitive data (which, in these plots, is the black…
Figure 28
Figure 28. Figure 28: Heatmap of similarity metrics based on the Total Variation Distance (TVD) between the datasets [PITH_FULL_IMAGE:figures/full_fig_p050_28.png]
Figure 29
Figure 29. Figure 29: Heatmap of similarity metrics based on the Average Error on 3-Way Marginals (3WM) between [PITH_FULL_IMAGE:figures/full_fig_p051_29.png]
Figure 30
Figure 30. Figure 30: Heatmap of similarity metrics based on the Total Variation Distance (TVD) between the datasets [PITH_FULL_IMAGE:figures/full_fig_p052_30.png]
Figure 31
Figure 31. Figure 31: Heatmap of similarity metrics based on the Average Error on 3-Way Marginals (3WM) between [PITH_FULL_IMAGE:figures/full_fig_p053_31.png]
Figure 32
Figure 32. Figure 32: Heatmap of similarity metrics based on the Total Variation Distance (TVD) between the datasets [PITH_FULL_IMAGE:figures/full_fig_p054_32.png]
Figure 33
Figure 33. Figure 33: Heatmap of similarity metrics based on the Average Error on 3-Way Marginals (3WM) between [PITH_FULL_IMAGE:figures/full_fig_p055_33.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.