Benchmarking Differentially Private Tabular Data Synthesis

• URL: http://arxiv.org/abs/2504.14061v1
• Date: Fri, 18 Apr 2025 20:27:23 GMT
• Title: Benchmarking Differentially Private Tabular Data Synthesis
• Authors: Kai Chen, Xiaochen Li, Chen Gong, Ryan McKenna, Tianhao Wang,
• Abstract summary: We present a benchmark for evaluating differentDP data synthesis methods.<n>Our evaluation reveals that a significant utility-efficiency trade-off exists among current state-of-the-art methods.<n>We conduct an in-depth analysis of each module with experimental validation, offering theoretical insights into the strengths and limitations of different strategies.
• Score: 21.320681813245525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differentially private (DP) tabular data synthesis generates artificial data that preserves the statistical properties of private data while safeguarding individual privacy. The emergence of diverse algorithms in recent years has introduced challenges in practical applications, such as inconsistent data processing methods, lack of in-depth algorithm analysis, and incomplete comparisons due to overlapping development timelines. These factors create significant obstacles to selecting appropriate algorithms. In this paper, we address these challenges by proposing a benchmark for evaluating tabular data synthesis methods. We present a unified evaluation framework that integrates data preprocessing, feature selection, and synthesis modules, facilitating fair and comprehensive comparisons. Our evaluation reveals that a significant utility-efficiency trade-off exists among current state-of-the-art methods. Some statistical methods are superior in synthesis utility, but their efficiency is not as good as most machine learning-based methods. Furthermore, we conduct an in-depth analysis of each module with experimental validation, offering theoretical insights into the strengths and limitations of different strategies.

Related papers

• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• Systematic Assessment of Tabular Data Synthesis Algorithms [9.08530697055844]
  We present a systematic evaluation framework for assessing data synthesis algorithms. We introduce a set of new metrics in terms of fidelity, privacy, and utility to address their limitations. Based on the proposed metrics, we also devise a unified objective for tuning, which can consistently improve the quality of synthetic data.
  arXiv  Detail & Related papers  (2024-02-09T22:07:59Z)
• 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)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• Partition-based differentially private synthetic data generation [0.5095097384893414]
  We present a partition-based approach that reduces errors and improves the quality of synthetic data, even with a limited privacy budget. The synthetic data produced using our approach exhibits improved quality and utility, making it a preferable choice for private synthetic data sharing.
  arXiv  Detail & Related papers  (2023-10-10T07:23:37Z)
• SoK: Privacy-Preserving Data Synthesis [72.92263073534899]
  This paper focuses on privacy-preserving data synthesis (PPDS) by providing a comprehensive overview, analysis, and discussion of the field. We put forth a master recipe that unifies two prominent strands of research in PPDS: statistical methods and deep learning (DL)-based methods.
  arXiv  Detail & Related papers  (2023-07-05T08:29:31Z)
• Differentially Private Synthetic Data Using KD-Trees [11.96971298978997]
  We exploit space partitioning techniques together with noise perturbation and thus achieve intuitive and transparent algorithms. We propose both data independent and data dependent algorithms for $epsilon$-differentially private synthetic data generation. We show empirical utility improvements over the prior work, and discuss performance of our algorithm on a downstream classification task on a real dataset.
  arXiv  Detail & Related papers  (2023-06-19T17:08:32Z)
• Detection and Evaluation of Clusters within Sequential Data [58.720142291102135]
  Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees. In particular, our sequential data is derived from human DNA, written text, animal movement data and financial markets. It is found that the Block Markov Chain model assumption can indeed produce meaningful insights in exploratory data analyses.
  arXiv  Detail & Related papers  (2022-10-04T15:22:39Z)
• Leachable Component Clustering [10.377914682543903]
  In this work, a novel approach to clustering of incomplete data, termed leachable component clustering, is proposed. The proposed method handles data imputation with Bayes alignment, and collects the lost patterns in theory. Experiments on several artificial incomplete data sets demonstrate that, the proposed method is able to present superior performance compared with other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2022-08-28T13:13:17Z)
• A Field Guide to Federated Optimization [161.3779046812383]
  Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms.
  arXiv  Detail & Related papers  (2021-07-14T18:09:08Z)
• Foundations of Bayesian Learning from Synthetic Data [1.6249267147413522]
  We use a Bayesian paradigm to characterise the updating of model parameters when learning on synthetic data. Recent results from general Bayesian updating support a novel and robust approach to synthetic-learning founded on decision theory.
  arXiv  Detail & Related papers  (2020-11-16T21:49:17Z)

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.