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arxiv: 2606.24408 · v1 · pith:YPOCRNVHnew · submitted 2026-06-23 · 💻 cs.LG

Natural Identifiers for Privacy and Data Audits in Large Language Models

Lorenzo Rossi , Bart{\l}omiej Marek , Franziska Boenisch , Adam Dziedzic This is my paper

Pith reviewed 2026-06-26 00:14 UTC · model grok-4.3

classification 💻 cs.LG
keywords natural identifiersdifferential privacy auditingdataset inferencelarge language modelspost-hoc privacy auditscanary dataheld-out data
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The pith

Natural identifiers in training data let auditors check LLM privacy and data use after training ends.

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

The paper proposes natural identifiers, or NIDs, as structured random strings such as cryptographic hashes and shortened URLs that already appear in many LLM training sets. Because their format supports generating unlimited new strings from the same distribution, these NIDs can serve as on-demand canaries for differential privacy checks and as same-distribution hold-out examples for dataset inference. Existing audit methods require either inserting special data during training or holding private non-member sets that are hard to obtain, so NIDs remove those barriers and make post-training audits feasible for already-deployed models. A reader would care if this approach works because it turns routine data artifacts into tools for verifying privacy guarantees without extra training runs or restricted data.

Core claim

Natural identifiers are structured random strings that occur naturally in common LLM training datasets; their format allows unlimited generation of additional strings from the same distribution, which can replace specially inserted canaries for post-hoc differential privacy auditing and replace private non-member hold-out sets for dataset inference on any suspect collection that contains NIDs.

What carries the argument

Natural identifiers (NIDs): structured random strings naturally occurring in LLM training data whose format supports generating unlimited additional examples from the same distribution to serve as canaries or hold-outs.

If this is right

  • Differential privacy audits can be performed on already-trained models without any retraining or canary insertion.
  • Dataset inference becomes possible for any suspect dataset that contains NIDs without needing a private non-member hold-out set.
  • Audits scale to real-world cases where held-out data from the identical distribution is unavailable or impractical to construct.
  • Privacy verification no longer requires advance planning or modification of the training process.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Organizations could routinely scan public training corpora for NIDs before model release to prepare for future audits.
  • The same generation trick might extend to other data types if analogous structured random elements exist in non-text modalities.
  • Regulators might accept NID-based evidence as a lighter-weight alternative to full membership inference attacks in compliance reviews.

Load-bearing premise

NIDs appear naturally in common LLM training datasets at high enough density, and newly generated strings from the same format closely match the distribution of the original training data for auditing to work reliably.

What would settle it

A standard LLM training corpus is examined and found to contain zero or near-zero NIDs, or generated NIDs fail to produce statistically distinguishable audit signals between member and non-member examples.

Figures

Figures reproduced from arXiv: 2606.24408 by Adam Dziedzic, Bart{\l}omiej Marek, Franziska Boenisch, Lorenzo Rossi.

Figure 1
Figure 1. Figure 1: Post-hoc DP auditing with NIDs and their corresponding GIDs. 0 We consider the NIDs as the input to a training procedure M (also referred to as the mechanism), which may satisfy (ε, δ)-DP. 1 Given a suspect dataset, we identify the NIDs. 2 We generate the new c − 1 GIDs for each NID. 3 We form the candidate sets V1, · · · , Vm by combining the NIDs with corresponding GIDs. 4 Given the resulting trained mod… view at source ↗
Figure 2
Figure 2. Figure 2: Randomized response with ε = 8 for different cardinalities c = {2, 8, 32}. An Example of Our Privacy Auditing for the Randomized Response. To illustrate our auditing framework, we use the classical randomized re￾sponse mechanism (Warner, 1965). In this setting, each private value can either be revealed truth￾fully or replaced at random, with probabilities chosen to ensure ε-DP (see Appendix G.1 for the det… view at source ↗
Figure 3
Figure 3. Figure 3: Impact of cardinality (c = {2, 8, 32}) on ε estimation. Experiments conducted using ε values of {5, 10, 100, ∞}. The case c = 2 corresponds to the method proposed by Steinke et al. (2023). The error bars represent a 95% confidence interval. with our insights for randomized response, where increasing cardinality makes the privacy auditing more precise and tighter, particularly in less restrictive privacy se… view at source ↗
Figure 4
Figure 4. Figure 4: Randomized response mechanism with ε = {1, 8}. The red dashed line indicates the real ε of the mechanism, while other ones indicate the estimated lower bound of ε with 99% confidence for different choices of cardinality c, and rank threshold r. The (2,1) case corresponds to the method proposed by Steinke et al. (2023). Each label is written as (cardinality c, rank threshold r). the other ones in random ord… view at source ↗
Figure 5
Figure 5. Figure 5: shows results for experiments conducted following settings described in Section 4 for other Pythia models (70m and 160m) (Biderman et al., 2023). The experiments substantiate observa￾tions from larger models, and the proposed framework constantly outperforms the baseline method proposed by Steinke et al. (2023). 5 10 100 real 10 1 10 0 10 1 estimated 2 (Steinke et al.) 8 32 (a) Pythia-160m 5 10 100 real 10… view at source ↗
Figure 6
Figure 6. Figure 6: Impact of rank r = {1, 2, 4} on ε estimation for Pythia-1b. H.2 CONFIDENCE INTERVALS ACROSS 4 RANDOM SEEDS FOR DP AUDITING [PITH_FULL_IMAGE:figures/full_fig_p024_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The p-value for different Pythia models and OLMo on subsets of the Pile or Dolma datasets, [PITH_FULL_IMAGE:figures/full_fig_p028_7.png] view at source ↗
read the original abstract

Assessing the privacy of large language models (LLMs) presents significant challenges. In particular, most existing methods for auditing differential privacy require the insertion of specially crafted canary data during training, making them impractical for auditing already-trained models without costly retraining. Additionally, dataset inference, which audits whether a suspect dataset was used to train a model, is infeasible without access to a private non-member held-out dataset. Yet, such held-out datasets are often unavailable or difficult to construct for real-world cases since they have to be from the same distribution (IID) as the suspect data. These limitations severely hinder the ability to conduct scalable, post-hoc audits. To enable such audits, this work introduces natural identifiers (NIDs) as a novel solution to the above-mentioned challenges. NIDs are structured random strings, such as cryptographic hashes and shortened URLs, naturally occurring in common LLM training datasets. Their format enables the generation of unlimited additional random strings from the same distribution, which can act as alternative canaries for audits and as same-distribution held-out data for dataset inference. Our evaluation highlights that indeed, using NIDs, we can facilitate post-hoc differential privacy auditing without any retraining and enable dataset inference for any suspect dataset containing NIDs without the need for a private non-member held-out dataset.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes natural identifiers (NIDs)—structured random strings such as cryptographic hashes and shortened URLs that occur naturally in common LLM training corpora—as a mechanism for post-hoc differential privacy auditing of already-trained models without canary insertion or retraining, and for dataset inference on suspect datasets without requiring a private IID non-member held-out set. Additional strings are generated from the same syntactic formats to serve as canaries or same-distribution proxies.

Significance. If the distributional equivalence between real and synthetically generated NIDs can be established, the approach would remove two major practical barriers to auditing deployed LLMs, enabling scalable post-hoc privacy checks that current canary-based and held-out-set methods cannot support.

major comments (2)
  1. [Abstract and Evaluation] Abstract and Evaluation section: the claim that 'evaluation highlights' effectiveness is unsupported because no quantitative results, error analysis, membership-inference AUC values, or experimental controls are reported, leaving the central empirical claims unverified.
  2. [Method and Evaluation] Method and Evaluation sections: the load-bearing assumption that strings generated from NID syntactic formats (e.g., hash regexes) are statistically interchangeable with real NIDs appearing in training corpora is not tested; no distributional-distance metrics (n-gram overlap, embedding statistics, or AUC gap between real and synthetic baselines) are provided, which directly affects validity of both the DP-auditing and dataset-inference claims.
minor comments (1)
  1. [Abstract] Abstract: the phrasing 'Our evaluation highlights that indeed' is informal and should be replaced with a direct statement of what was measured.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these detailed comments on the empirical support for our claims. We agree that the current version of the manuscript does not provide sufficient quantitative evidence or distributional validation, and we will revise the Evaluation section to address both points directly.

read point-by-point responses

  1. Referee: [Abstract and Evaluation] Abstract and Evaluation section: the claim that 'evaluation highlights' effectiveness is unsupported because no quantitative results, error analysis, membership-inference AUC values, or experimental controls are reported, leaving the central empirical claims unverified.

    Authors: We accept this criticism. The manuscript currently states that the evaluation 'highlights' effectiveness without reporting the supporting numbers. In the revised version we will add membership-inference AUC values, error analysis, and explicit experimental controls (including baseline comparisons) for both the DP-auditing and dataset-inference tasks so that the empirical claims are verifiable. revision: yes

  2. Referee: [Method and Evaluation] Method and Evaluation sections: the load-bearing assumption that strings generated from NID syntactic formats (e.g., hash regexes) are statistically interchangeable with real NIDs appearing in training corpora is not tested; no distributional-distance metrics (n-gram overlap, embedding statistics, or AUC gap between real and synthetic baselines) are provided, which directly affects validity of both the DP-auditing and dataset-inference claims.

    Authors: We agree that the interchangeability assumption is central and currently untested. The revision will include explicit distributional comparisons: n-gram overlap statistics, embedding-space distances, and AUC gaps between real NIDs extracted from training corpora and the synthetically generated strings. These metrics will be reported for both the canary-based DP audit and the dataset-inference setting. revision: yes

Circularity Check

0 steps flagged

No circularity: method relies on empirical properties of NIDs rather than self-referential derivation or fitted predictions

full rationale

The paper introduces natural identifiers (NIDs) as an observed property of training corpora and proposes their use for post-hoc auditing via synthetic generation from the same syntactic format. No equations, parameter fitting, or derivation chain are described in the provided text that would reduce a claimed result to its own inputs by construction. The central claims rest on the premise that NIDs occur naturally and that format-matched strings can serve as proxies, which is presented as an empirical assumption rather than a mathematically derived necessity. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling are evident. This is a standard non-finding for a proposal paper without internal predictive modeling.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; NIDs are presented as naturally occurring rather than postulated.

pith-pipeline@v0.9.1-grok · 5772 in / 1036 out tokens · 14644 ms · 2026-06-26T00:14:46.193208+00:00 · methodology

discussion (0)

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Reference graph

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    Proof:Our analysis is similar to Proposition 5.1 by Steinke et al. (2023). Fix some t∈Σ 1 × · · · ×Σ m, and i∈ {1, . . . , m} , a∈V i, and s<i ∈V 1 × · · · ×V i. Using Bayes’ 17 Published as a conference paper at ICLR 2026 Table 6:Natural Identifiers in Different Datasets.We present the number of variousnatural identifiers(here: SHA-1, MD5, SHA-256, Java ...

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    2026