metadata

license: cc-by-sa-4.0
size_categories:
  - 10M<n<100M
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - criteo
  - advertising

Dataset Documentation

Private Bidding Optimisation {#private-conversion-optimisation}

The advertising industry lacks a common benchmark to assess the privacy / utility trade-off in private advertising systems. To fill this gap, we are open-sourcing CriteoPrivateAd, the largest real-world anonymised bidding dataset, in terms of number of features. This dataset enables engineers and researchers to:

assess the impact of removing cross-domain user signals, highlighting the effects of third-party cookie deprecation;
design and test private bidding optimisation approaches using contextual signals and user features;
evaluate the relevancy of answers provided by aggregation APIs for bidding model learning.

Summary

This dataset is released by Criteo to foster research and industrial innovation on privacy-preserving machine learning applied to a major advertising use-case, namely bid optimisation under user signal loss / obfuscation.

This use-case is inspired by challenges both browser vendors and AdTech companies are facing due to third-party cookie deprecation, such as ensuring a viable cookie-less advertising business via a pragmatic performance / privacy trade-off. In particular, we are expecting to see improvements of Google Chrome Privacy Sandbox and Microsoft Ad Selection APIs via offline benchmarks based on this dataset.

The dataset contains an anonymised log aiming to mimic production performance of AdTech bidding engines, so that offline results based on this dataset could be taken as ground truth to improve online advertising performance under privacy constraints. Features are grouped into several groups depending on their nature, envisioned privacy constraints and availability at inference time.

Based on this dataset, the intended objective is to implement privacy constraints (e.g. by aggregating labels or by adding differential privacy to features and/or labels) and then learn click and conversion (e.g. sales) prediction models.

The associated paper is available here

As a leading AdTech company that drives commerce outcomes for media owners and marketers, Criteo is committed to evaluating proposals that might affect the way we will perform attribution, reporting and campaign optimisation in the future. Criteo has already participated in testing and providing feedback on browser proposals such as the Privacy Sandbox one; see all our Medium articles Back in 2021, we also organised a public challenge aiming to assess bidding performance when learning on aggregated data: our learnings are available here.

Dataset Description

A precise description of the dataset and each column is available in the companion paper

This dataset represents a 100M anonymised sample of 30 days of Criteo live data retrieved from third-party cookie traffic on Chrome. Each line corresponds to one impression (a banner) that was displayed to a user. It is partionned by day (day_int) to facilitate exploration, model seeding and train/validation/test split.

For each impression, we are providing:

campaign x publisher x (user x day) granularity with respective ids, to match Chrome Privacy Sandbox scenarios and both display and user-level privacy.
4 labels (click, click leading to a landing on an advertiser website, click leading to a visit on an advertiser website - i.e. landing followed by one advertiser event, number of sales attributed to the clicked display).
more than 100 features grouped in 5 buckets with respect to their logging and inference constraints in Protected Audience API from Chrome Privacy Sandbox (note that these buckets are generic enough to cover other private advertising frameworks as we are mainly providing a split between ad campaign features, single-domain & cross-domain user features, and contextual features) :
- Features available in the key-value server with 12-bit logging constraint (i.e. derived from current version of modelingSignals and standing for single-domain user features).
- Features available in the key-value server with no logging constraint (i.e. derived from Interest Group name / renderURL).
- Features available in browser with 12-bit constraint (i.e. cross-domain features available in generateBid).
- Features from contextual call with no logging constraint (i.e. contextual features).
- Features not available (i.e. cross-device and cross-domain ones).
day_int enabling (1) splitting the log into training, validation and testing sets; (2) performing relevant model seeding.
Information about conversion delay to simulate the way Privacy Sandbox APIs are working.
time_between_request_timestamp_and_post_display_event (column name in clear): time delta (in minutes) between the request timestamp and the click or sale event. All displays are considered starting the day of the event at 00:00 to avoid providing complete timelines.
We include a display order from 1 to K for display on the same day for the same user.

The displays-per-user histograms can be deduced from event_per_user_contribution.csv. It is useful to build importance sampling ratios and user-level DP, as it is detailed in the companion paper.

Metrics

The metrics best suited to the click and conversion estimation problems are:

the log-likelihood (LLH), and preferably a rescaled version named LLH-CompVN defined as the relative log-likelihood uplift compared to the naive model always predicting the average label in the training dataset;
calibration, defined as the ratio between the sum of the predictions and the sum of the validation labels. It must be close to 1 for a bidding application;

We would like to point out that conventional classification measures such as area under the curve (AUC) are less relevant for comparing auction models.

The click-through rate is higher than the one encountered in real-world advertising systems on the open internet. To design realistic bidding applications, one must use a weighted loss for validation. We defer the interested readers to the associated companion paper for more details

Baselines

The Training period has been fixed to 1->25 and Validation period to 26->30. The chosen loss is the LLH-CompVN with weighting as defined above. The Sales | Display is a product of the Landed Click | Display and the Sales | Landed Click.

Task/CTR	0.1%	0.5%	1%
Landed Click \| Display	0.170	0.186	0.234
Sales \| Landed Click	0.218	0.218	0.218
Sales \| Display	0.171	0.187	0.237

Note that our baseline results might be difficult to achieve because of the anonymisation of the dataset.

License

The data is released under the license. You are free to Share and Adapt this data provided that you respect the Attribution and ShareAlike conditions. Please read carefully the full license before using.

Citation

If you use the dataset in your research please cite it using the following Bibtex excerpt:

@misc{sebbar2025criteoprivateadrealworldbiddingdataset,
  title={CriteoPrivateAd: A Real-World Bidding Dataset to Design Private Advertising Systems},
  author={Mehdi Sebbar and Corentin Odic and Mathieu Léchine and Aloïs Bissuel and 
          Nicolas Chrysanthos and Anthony D'Amato and Alexandre Gilotte and 
          Fabian Höring and Sarah Nogueira and Maxime Vono},
  year={2025},
  eprint={2502.12103},
  archivePrefix={arXiv},
  primaryClass={cs.CR},
  url={https://arxiv.org/abs/2502.12103},
}

Acknowledgment

We would like to thank:

Google Chrome Privacy Sandbox team, especially Charlie Harrison, for feedbacks on the usefulness of this dataset.
W3C PATCG group, notably for their public data requests to foster work on the future of attribution and reporting.
Criteo stakeholders who took part of this dataset release: Anthony D'Amato, Mathieu Léchine, Mehdi Sebbar, Corentin Odic, Maxime Vono, Camille Jandot, Fatma Moalla, Nicolas Chrysanthos, Romain Lerallut, Alexandre Gilotte, Aloïs Bissuel, Lionel Basdevant, Henry Jantet.