Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasets

• URL: http://arxiv.org/abs/2502.13833v1
• Date: Wed, 19 Feb 2025 15:52:23 GMT
• Title: Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasets
• Authors: Milton Nicolás Plasencia Palacios, Sebastiano Saccani, Gabriele Sgroi, Alexander Boudewijn, Luca Bortolussi,
• Abstract summary: Synthetic data has garnered attention as a Privacy Enhancing Technology (PET) in sectors such as healthcare and finance. Similarity-based methods aim at finding the level of similarity between training and synthetic data. Attack-based methods conduce deliberate attacks on synthetic datasets.
• Score: 40.67424997797513
• License:
• Abstract: Synthetic data has garnered attention as a Privacy Enhancing Technology (PET) in sectors such as healthcare and finance. When using synthetic data in practical applications, it is important to provide protection guarantees. In the literature, two family of approaches are proposed for tabular data: on the one hand, Similarity-based methods aim at finding the level of similarity between training and synthetic data. Indeed, a privacy breach can occur if the generated data is consistently too similar or even identical to the train data. On the other hand, Attack-based methods conduce deliberate attacks on synthetic datasets. The success rates of these attacks reveal how secure the synthetic datasets are. In this paper, we introduce a contrastive method that improves privacy assessment of synthetic datasets by embedding the data in a more representative space. This overcomes obstacles surrounding the multitude of data types and attributes. It also makes the use of intuitive distance metrics possible for similarity measurements and as an attack vector. In a series of experiments with publicly available datasets, we compare the performances of similarity-based and attack-based methods, both with and without use of the contrastive learning-based embeddings. Our results show that relatively efficient, easy to implement privacy metrics can perform equally well as more advanced metrics explicitly modeling conditions for privacy referred to by the GDPR.

Related papers

• Synthetic Data Privacy Metrics [2.1213500139850017]
  We review the pros and cons of popular metrics that include simulations of adversarial attacks. We also review current best practices for amending generative models to enhance the privacy of the data they create.
  arXiv  Detail & Related papers  (2025-01-07T17:02:33Z)
• Tabular Data Synthesis with Differential Privacy: A Survey [24.500349285858597]
  Data sharing is a prerequisite for collaborative innovation, enabling organizations to leverage diverse datasets for deeper insights. Data synthesis tackles this by generating artificial datasets that preserve the statistical characteristics of real data. Differentially private data synthesis has emerged as a promising approach to privacy-aware data sharing.
  arXiv  Detail & Related papers  (2024-11-04T06:32:48Z)
• Efficient Data Collection for Robotic Manipulation via Compositional Generalization [70.76782930312746]
  We show that policies can compose environmental factors from their data to succeed when encountering unseen factor combinations. We propose better in-domain data collection strategies that exploit composition. We provide videos at http://iliad.stanford.edu/robot-data-comp/.
  arXiv  Detail & Related papers  (2024-03-08T07:15:38Z)
• Reliability in Semantic Segmentation: Can We Use Synthetic Data? [69.28268603137546]
  We show for the first time how synthetic data can be specifically generated to assess comprehensively the real-world reliability of semantic segmentation models. This synthetic data is employed to evaluate the robustness of pretrained segmenters. We demonstrate how our approach can be utilized to enhance the calibration and OOD detection capabilities of segmenters.
  arXiv  Detail & Related papers  (2023-12-14T18:56:07Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning [3.29354893777827]
  We introduce a data release framework, 3A (Approximate, Adapt, Anonymize), to maximize data utility for machine learning. We present experimental evidence showing minimal discrepancy between performance metrics of models trained on real versus privatized datasets.
  arXiv  Detail & Related papers  (2023-07-04T18:37:11Z)
• Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing [9.061271587514215]
  We propose a principled vulnerable record identification technique for synthetic data publishing. We show it to strongly outperform previous ad-hoc methods across datasets and generators. We show it to accurately identify vulnerable records when synthetic data generators are made differentially private.
  arXiv  Detail & Related papers  (2023-06-17T09:42:46Z)
• Synthetic Data: Methods, Use Cases, and Risks [11.413309528464632]
  A possible alternative gaining momentum in both the research community and industry is to share synthetic data instead. We provide a gentle introduction to synthetic data and discuss its use cases, the privacy challenges that are still unaddressed, and its inherent limitations as an effective privacy-enhancing technology.
  arXiv  Detail & Related papers  (2023-03-01T16:35:33Z)
• Membership Inference Attacks against Synthetic Data through Overfitting Detection [84.02632160692995]
  We argue for a realistic MIA setting that assumes the attacker has some knowledge of the underlying data distribution. We propose DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.
  arXiv  Detail & Related papers  (2023-02-24T11:27:39Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)

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.