---
license: cc-by-nc-4.0
language:
- en
pretty_name: "On-Device Auction Audit: Information and Incentive Misalignment When Ad Auctions Move On Device"
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
tags:
- auctions
- ad-tech
- on-device
- privacy
- mechanism-design
- simulation
- wildchat
- reproducibility
configs:
- config_name: primary_replicates
  default: true
  data_files:
  - split: synthetic
    path: data/primary_replicates/synthetic.jsonl
  - split: ipinyou
    path: data/primary_replicates/ipinyou.jsonl
- config_name: pressure_balance
  data_files:
  - split: train
    path: data/pressure_balance/train.jsonl
- config_name: bursty_heterogeneous
  data_files:
  - split: train
    path: data/bursty_heterogeneous/train.jsonl
- config_name: replicate_summary
  data_files:
  - split: synthetic
    path: data/replicate_summary/synthetic.jsonl
  - split: ipinyou
    path: data/replicate_summary/ipinyou.jsonl
- config_name: per_campaign
  data_files:
  - split: synthetic
    path: data/per_campaign/synthetic.jsonl
  - split: ipinyou
    path: data/per_campaign/ipinyou.jsonl
- config_name: calibration
  data_files:
  - split: synthetic
    path: data/calibration/synthetic.jsonl
  - split: ipinyou
    path: data/calibration/ipinyou.jsonl
- config_name: factorial_ablation
  data_files:
  - split: synthetic
    path: data/factorial_ablation/synthetic.jsonl
  - split: ipinyou
    path: data/factorial_ablation/ipinyou.jsonl
- config_name: factorial_calibration
  data_files:
  - split: synthetic
    path: data/factorial_calibration/synthetic.jsonl
  - split: ipinyou
    path: data/factorial_calibration/ipinyou.jsonl
- config_name: sensitivity
  data_files:
  - split: synthetic
    path: data/sensitivity/synthetic.jsonl
  - split: ipinyou
    path: data/sensitivity/ipinyou.jsonl
- config_name: label_perturbation
  data_files:
  - split: synthetic
    path: data/label_perturbation/synthetic.jsonl
- config_name: label_perturbation_calibration
  data_files:
  - split: synthetic
    path: data/label_perturbation_calibration/synthetic.jsonl
- config_name: contexts
  data_files:
  - split: train
    path: data/contexts/part-*.jsonl
---

# On-Device Auction Audit

Data for **When Privacy Moves ML-Mediated Decisions On Device: Information and Incentive Misalignment in Auctions**, Dipankar Sarkar, Skelf Research.

Paper: https://arxiv.org/abs/2609.33312

Code and full artifact: [github.com/sarkar-dipankar/on-device-auction-audit](https://github.com/sarkar-dipankar/on-device-auction-audit) (this package is built from commit `1d1a05b`; the data files are unchanged since `5a6ad86`).

## The question

Privacy pushes ad decisions onto the device. The auction runs locally, next to the context it scores.

But the budget lives on a server.

So each device bids against a balance it last saw at its most recent sync. Between syncs, campaigns can spend money they no longer have. And the local auction can reward a bidder for misreporting.

The paper asks how much that costs. Two misalignments, measured side by side:

- **Information**: stale budget state. Measured as `overspend_pct` and `underspend_pct` against a frozen budget.
- **Incentive**: auctions the simulator flags as manipulable or misallocated. Measured as `manipulable_auction_rate`, `manipulable_rival_auction_rate`, and `misallocation_rate`.

## What the simulation does

Every primary run uses the same frame, read from the manifests:

- 50 devices, 36 campaigns, 100 ticks, 3,000 expected arrivals per tick (about 300,000 auctions per run).
- Each arrival samples one ad context uniformly from the 50,808 labelled windows in the `contexts` config.
- Budget sync intervals of 0, 1, 2, 5, 10, 25, and 50 ticks. Interval 0 is the zero-lag reference.
- Pacing strategies `asap`, `even`, `even_bernoulli`, and `pid`. Payment rules `second_score` and `critical_bid`.
- Budgets are calibrated on 30 pilot seeds (30260527 to 30260556) to a target oversubscription of 20, then frozen. Results come from 30 disjoint evaluation seeds (20260527 to 20260556).

Two demand constructions share that frame. They are the two splits of most configs:

- `synthetic`: a fixed value draw (`bid_distribution` kind `fixed`, 100 score units). The release directory is `track_b_smoke`, a legacy name.
- `ipinyou`: a log-normal draw with mu 3.8713 and sigma 0.85, set so its mean equals the 68.89 mean market price reported for iPinYou campaign 1458 (Zhang et al. 2014). A calibrated synthetic value proxy, not raw bidding logs.

One cut you can recompute from `primary_replicates` in a few lines. Mean `overspend_pct` over 30 seeds, `even` pacing, `second_score` payment:

| Sync interval (ticks) | 0 | 1 | 2 | 5 | 10 | 25 | 50 |
|---|---|---|---|---|---|---|---|
| synthetic | 0.53 | 17.77 | 50.43 | 189.94 | 479.28 | 1346.13 | 1669.31 |
| ipinyou | 1.00 | 31.87 | 72.14 | 109.44 | 124.08 | 402.79 | 905.03 |

Lag alone moves total spend from about 1% over the frozen budget to 10.1 times the budget (ipinyou) or 17.7 times (synthetic) at 50 ticks. The paper's bounds, paired contrasts, and confidence intervals are in `raw/results/track_b_paper_numbers.json`.

## Configs

Twelve viewer configs, all JSONL, built by `build_hf.py` (in this repo, stdlib only, deterministic). Rows keep their source order. The original files are unchanged under `raw/results/`.

| Config | Splits (rows) | Source | What it is |
|---|---|---|---|
| `primary_replicates` (default) | synthetic 900, ipinyou 900 | `track_b_{smoke,ipinyou}/replicates.jsonl` | The primary result. One row per strategy, payment rule, sync interval, and seed: spend, overspend, underspend, fill, clearing price, delivered value, manipulability, misallocation. 30 cells x 30 seeds per split. |
| `pressure_balance` | train 1,050 | `track_b_pressure_balance/replicates.jsonl` | 840 `pressure_sensitivity` rows (budget pressure 2, 5, 10, 20) and 210 `visible_balance_guard` rows (devices reject bids above their visible balance, pressure 20). `even` pacing, 7 sync intervals, 30 seeds. |
| `bursty_heterogeneous` | train 210 | `track_b_bursty_heterogeneous/replicates.jsonl` | Robustness sweep. Two-state Markov-modulated demand over 10 heavy and 40 light devices, stationary mean load unchanged. 7 sync intervals x 30 seeds. |
| `replicate_summary` | synthetic 30, ipinyou 30 | `replicate_summary.jsonl` | One row per primary cell: mean, sd, min, max, and Student-t 95% CI of each metric over its 30 seeds. |
| `per_campaign` | synthetic 216, ipinyou 216 | `per_campaign.jsonl` | Per-campaign budget, spend, wins, and delivered value for `even` pacing at sync intervals 1 to 50. |
| `calibration` | synthetic 744, ipinyou 744 | `calibration.jsonl` | The budget calibration trail: 720 pilot runs and 24 frozen per-vertical budgets, for the `second_score` and `critical_bid` (reserve 100) designs. |
| `factorial_ablation` | synthetic 240, ipinyou 240 | `factorial_ablation.jsonl` | Payment rule x budget policy (calibrated or floored at 500) x reserve (0 or 100), zero lag, 30 seeds per cell. |
| `factorial_calibration` | synthetic 384, ipinyou 384 | `factorial_calibration.jsonl` | Calibration rows for the factorial budget vectors. |
| `sensitivity` | synthetic 180, ipinyou 180 | `sensitivity.jsonl` | Device count (10, 50, 200) and aggregate arrival load (1,500, 3,000, 6,000) varied one at a time, 30 seeds per level. |
| `label_perturbation` | synthetic 210 | `track_b_smoke/label_perturbation.jsonl` | Combined label stress test on the context labels, 7 sync intervals x 30 seeds. |
| `label_perturbation_calibration` | synthetic 372 | `track_b_smoke/label_perturbation_calibration.jsonl` | Budget calibration for the perturbed labels (360 pilot runs, 12 frozen summaries). |
| `contexts` | train 50,808 | `data/opportunities_teacher_50k.jsonl` | The frozen context labels every run samples from. Two shards. |

### The `contexts` config

One row per WildChat-1M conversation window (`window_id`, all unique). No conversation text.

- `topic`: 24 values. `technology` is the largest (18,394 rows).
- `intent_stage`: `none`, `informational`, `comparison`, `purchase_ready`.
- `category`: 19 ad categories, null on 41,247 rows.
- `safety_class`: 8 values, `ok` on 45,886 rows.
- `ad_eligible`: true on 6,370 rows.
- `subtopic` is null on every row. `locale` (`en-US`) and `device_type` (`desktop`) are constant. They are kept so the schema matches the simulator input.

The file is 9.9 MB, so it is split on line boundaries into `data/contexts/part-0000.jsonl` and `part-0001.jsonl` (25,404 rows each). The shards are byte-exact slices. Concatenated in order they reproduce the source file, and its sha256 matches the one every manifest records:

```sh
cat data/contexts/part-0000.jsonl data/contexts/part-0001.jsonl | sha256sum
# 16ad2a17e55bb2185bfe4633521c70c9c03962131bc430358cc13df018ecbbf8
```

The label stress test in `label_perturbation` changes 1,558 of the 50,808 records: 525 false positives, 508 false negatives, and 525 wrong-vertical relabels (`raw/results/track_b_smoke/label_perturbation_manifest.json`). The manifest calls it a sensitivity analysis, not an estimated label-error model.

### Column notes

- **Accounting uses dimensionless integer score units.** The `*_cents` identifiers are legacy Rust field names. They carry no currency semantics. The newer runs (`pressure_balance`, `bursty_heterogeneous`) already use `*_score_units`.
- Nested values are serialised as JSON strings so each column has one type. That covers every metric in `replicate_summary` (a `{mean, sd, min, max, ci95_halfwidth, ci95_method}` object) and `campaign_exhaustion_fractions` in `primary_replicates` (one value per campaign). Parse them with `json.loads`.
- `sensitivity` holds two designs in one file. `aggregate_arrivals_held_fixed` is set only on `n_devices` rows and `budget_vector_held_fixed` only on `aggregate_arrival_load` rows. Each is null elsewhere.
- `pilot_index` in `label_perturbation_calibration` is null on the 12 `frozen_summary` rows.

### raw/

`raw/results/` is an exact copy of the release `results/` directory. `raw/scripts/` holds the paper's offline generators and tests. `raw/data/ipinyou_1458.json` holds the published iPinYou aggregates. `raw/SHA256SUMS` and `raw/LICENSE` are the release's checksum and license files. No config points at `raw/`.

It also holds files with no config:

- `track_b_paper_numbers.json` and `track_b_revision_numbers.json`, the statistical summaries the paper cites.
- Every `manifest.json`, with seeds, budgets, commands, input hashes, and source hashes.
- `budgets`, `oracle`, `pacing_report`, and `variants` JSONL files (5 to 36 rows each).
- `track_b_controllers/`, a legacy floored-budget controller export. Its manifest has status `superseded` by `track_b_smoke` and `track_b_ipinyou` and says it is not an input to the paper. It is kept for completeness and given no config.

## Withheld simulator source

The simulator is not public. Each manifest names a `source_snapshot.tar.gz` holding the code that produced its rows. Those archives are not in the GitHub release and not in this package. Their SHA-256 values stay in the manifests as a record of the exact code state:

- primary and iPinYou runs: `dabe08d17b18aa6ba8816b99b4ed164dc866c3de12b9eee3439e55d5da72a2a4`
- pressure/visible-balance and bursty/heterogeneous runs: `207a49793ebeab890dd6821deebde1138d569d8c0f55c32c7961f95597a1cebc`

So the simulations cannot be replayed from this repo. The statistics can.

## Reproduce the statistics

From a clone of [the GitHub repo](https://github.com/sarkar-dipankar/on-device-auction-audit):

```sh
python3 -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python scripts/generate_track_b_paper_numbers.py
python scripts/generate_track_b_revision_numbers.py
python scripts/test_track_b_analysis.py
```

We re-ran this on a copy of commit `5a6ad86` (Python 3.12.3, SciPy 1.18.0). Both generators passed their input checks. All 5 tests passed.

The regenerated summary JSONs differ in bytes from `SHA256SUMS`. Every non-float value is identical. The float differences are rounding noise: at most 1.03e-13 relative in `track_b_paper_numbers.json` and 2.2e-14 in `track_b_revision_numbers.json`. Compare them numerically, not by hash.

To rebuild this package from a checkout:

```sh
python3 build_hf.py /path/to/on-device-auction-audit .
```

## Provenance

- **Contexts** derive from [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M) (Zhao et al., ICLR 2024, [arXiv:2405.01470](https://arxiv.org/abs/2405.01470)). Each window was labelled by a single `gpt-oss:120b` teacher, described in the paper. **The labels are teacher labels, not human ground truth.** No conversation text is redistributed. See the WildChat paper and dataset card for collection and consent details.
- **iPinYou parameters** are published aggregates for campaign 1458 from Zhang et al. 2014, [arXiv:1407.7073](https://arxiv.org/abs/1407.7073), Table 3. No raw bidding logs are included. The market price is a value proxy, not advertiser private value.
- **Replicates** are simulator outputs produced by the withheld source above.

## License

The data is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).

The context labels derive from WildChat-1M and inherit its CC BY-NC 4.0 terms. Every simulation result here is computed from them, so the whole dataset carries those terms. Attribute both WildChat and this derivative.

The GitHub repo's code is MIT (`raw/LICENSE`). That covers `raw/scripts/` and `build_hf.py`. It does not cover the WildChat-derived data, and neither does the paper's arXiv license.

## Citation

```bibtex
@misc{sarkar2026ondevice,
  title = {When Privacy Moves ML-Mediated Decisions On Device: Information and Incentive Misalignment in Auctions},
  author = {Dipankar Sarkar},
  year = {2026},
  eprint = {2609.33312},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.33312}
}
```

Please also cite WildChat (Zhao et al., ICLR 2024) and, if you use the `ipinyou` split, Zhang et al. 2014.

## Links

- Paper: https://arxiv.org/abs/2609.33312
- Code and full artifact: https://github.com/sarkar-dipankar/on-device-auction-audit
- WildChat-1M: https://huggingface.co/datasets/allenai/WildChat-1M