Unlocking Post-hoc Dataset Inference with Synthetic Data

• URL: http://arxiv.org/abs/2506.15271v1
• Date: Wed, 18 Jun 2025 08:46:59 GMT
• Title: Unlocking Post-hoc Dataset Inference with Synthetic Data
• Authors: Bihe Zhao, Pratyush Maini, Franziska Boenisch, Adam Dziedzic,
• Abstract summary: Training datasets are often scraped from the internet without respecting data owners' intellectual property rights.<n>Inference (DI) offers a potential remedy by identifying whether a suspect dataset was used in training.<n>Existing DI methods require a private set-known to be absent from training-that closely matches the compromised dataset's distribution.<n>In this work, we address this challenge by synthetically generating the required held-out set.
• Score: 11.886166976507711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The remarkable capabilities of Large Language Models (LLMs) can be mainly attributed to their massive training datasets, which are often scraped from the internet without respecting data owners' intellectual property rights. Dataset Inference (DI) offers a potential remedy by identifying whether a suspect dataset was used in training, thereby enabling data owners to verify unauthorized use. However, existing DI methods require a private set-known to be absent from training-that closely matches the compromised dataset's distribution. Such in-distribution, held-out data is rarely available in practice, severely limiting the applicability of DI. In this work, we address this challenge by synthetically generating the required held-out set. Our approach tackles two key obstacles: (1) creating high-quality, diverse synthetic data that accurately reflects the original distribution, which we achieve via a data generator trained on a carefully designed suffix-based completion task, and (2) bridging likelihood gaps between real and synthetic data, which is realized through post-hoc calibration. Extensive experiments on diverse text datasets show that using our generated data as a held-out set enables DI to detect the original training sets with high confidence, while maintaining a low false positive rate. This result empowers copyright owners to make legitimate claims on data usage and demonstrates our method's reliability for real-world litigations. Our code is available at https://github.com/sprintml/PostHocDatasetInference.

Related papers

• Privacy-Preserving Methods for Bug Severity Prediction [0.0]
  We investigate method-level bug severity prediction using source code metrics and Large Language Models.<n>We compare the performance of models trained using centralized learning, federated learning, and synthetic data generation.<n>Our finding highlights the potential of privacy-preserving approaches to enable effective bug severity prediction in industrial context.
  arXiv  Detail & Related papers  (2025-06-28T04:40:51Z)
• A Certified Unlearning Approach without Access to Source Data [4.585544474674649]
  We propose a certified unlearning framework that enables effective data removal.<n>Our approach utilizes a surrogate dataset that approximates the statistical properties of the source data.<n>Results demonstrate the effectiveness and reliability of our approach in privacy-sensitive settings.
  arXiv  Detail & Related papers  (2025-06-06T19:22:47Z)
• Hey, That's My Data! Label-Only Dataset Inference in Large Language Models [63.35066172530291]
  CatShift is a label-only dataset-inference framework.<n>It capitalizes on catastrophic forgetting: the tendency of an LLM to overwrite previously learned knowledge when exposed to new data.
  arXiv  Detail & Related papers  (2025-06-06T13:02:59Z)
• Data Taggants: Dataset Ownership Verification via Harmless Targeted Data Poisoning [12.80649024603656]
  This paper introduces data taggants, a novel non-backdoor dataset ownership verification technique. We validate our approach through comprehensive and realistic experiments on ImageNet1k using ViT and ResNet models with state-of-the-art training recipes.
  arXiv  Detail & Related papers  (2024-10-09T12:49:23Z)
• Fact Checking Beyond Training Set [64.88575826304024]
  We show that the retriever-reader suffers from performance deterioration when it is trained on labeled data from one domain and used in another domain. We propose an adversarial algorithm to make the retriever component robust against distribution shift. We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models.
  arXiv  Detail & Related papers  (2024-03-27T15:15:14Z)
• Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks [70.39633252935445]
  Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora. For closed models, the training data becomes a trade secret, and even for open models, it is not trivial to detect contamination. We propose three strategies that can make a difference: (1) Test data made public should be encrypted with a public key and licensed to disallow derivative distribution; (2) demand training exclusion controls from closed API holders, and protect your test data by refusing to evaluate without them; and (3) avoid data which appears with its solution on the internet, and release the web-page context of internet-derived
  arXiv  Detail & Related papers  (2023-05-17T12:23:38Z)
• Data-SUITE: Data-centric identification of in-distribution incongruous examples [81.21462458089142]
  Data-SUITE is a data-centric framework to identify incongruous regions of in-distribution (ID) data. We empirically validate Data-SUITE's performance and coverage guarantees.
  arXiv  Detail & Related papers  (2022-02-17T18:58:31Z)
• ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning [52.831894583501395]
  Continual learning assumes the incoming data are fully labeled, which might not be applicable in real applications. We propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN) We show ORDisCo achieves significant performance improvement on various semi-supervised learning benchmark datasets for SSCL.
  arXiv  Detail & Related papers  (2021-01-02T09:04:14Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)
• DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier [58.979104709647295]
  We bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a trained network. We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples. We demonstrate that data from a related domain can be leveraged to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2019-12-27T02:05:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.