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Comprehensive Evaluation of Tabular Data Synthesis Algorithms: Balancing Fidelity, Privacy, and Utility

Core Concepts
This paper presents a systematic evaluation framework for assessing the performance of tabular data synthesis algorithms across three key dimensions: fidelity, privacy, and utility. The authors propose new metrics to address the limitations of existing evaluation approaches and conduct extensive experiments on a wide range of state-of-the-art synthesis algorithms.
The paper starts by highlighting the importance of data synthesis as an approach for utilizing data while protecting privacy. It then discusses the limitations of existing evaluation metrics and the lack of comprehensive comparisons among the growing number of synthesis algorithms. To address these issues, the authors propose a systematic evaluation framework called SynMeter, which assesses synthesis algorithms along three main axes: Fidelity: Existing metrics like low-order statistics and correlation measures are critiqued for their lack of versatility and inability to capture the overall distribution discrepancy. A new fidelity metric based on Wasserstein distance is introduced, which can handle both numerical and categorical attributes under a unified criterion. Privacy: The shortcomings of popular similarity-based privacy metrics like Distance to Closest Records (DCR) are identified, including their inability to provide worst-case protection and potential for information leakage. A novel privacy metric called Membership Disclosure Score (MDS) is proposed, which directly quantifies the disclosure risk by measuring the sensitivity of synthetic data to the inclusion of individual records. Utility: The prevalent use of machine learning efficacy as the utility metric is critiqued for its dependence on the choice of evaluation models, which can lead to inconsistent and misleading conclusions. Two new utility metrics are introduced: Machine Learning Affinity (MLA) to measure the distribution shift of synthetic data, and Query Error to assess the accuracy of range and point queries. The authors also introduce a unified tuning objective that can consistently improve the performance of synthesis algorithms across all three evaluation dimensions. Extensive experiments are conducted on 12 real-world datasets, covering a wide range of state-of-the-art heuristically private (HP) and differentially private (DP) synthesis algorithms. The key findings include: Diffusion models like TabDDPM excel at generating highly authentic tabular data, but suffer from significant membership privacy risks. Statistical methods like PGM and PrivSyn remain competitive, especially when DP is required or the privacy budget is small. Large language model-based synthesizer GReaT performs well on datasets with rich semantic attributes. CTGAN, a widely recognized baseline, struggles to learn marginal distributions on complex tabular data. The proposed SynMeter framework provides a systematic and modular toolkit for assessing, tuning, and benchmarking tabular data synthesis algorithms, facilitating further research and practical applications in this domain.
The Wasserstein distance between synthetic and real data distributions on the training set ranges from 0.010 to 0.157 across different synthesis algorithms and datasets. The membership disclosure score (MDS), which measures the maximum privacy risk across all records, varies significantly among the synthesis algorithms, from 1.2 to 4.5. The machine learning affinity (MLA), which quantifies the distribution shift of synthetic data for various ML models, ranges from 0.02 to 0.15 across different algorithms and datasets. The query error, which measures the accuracy of range and point queries on synthetic data, ranges from 0.05 to 0.35 for different synthesis methods.
"Diffusion models are surprisingly good at synthesizing tabular data. Although they were originally introduced for image generation, our evaluation indicates their impressive capability of synthesizing highly authentic tabular data." "Statistical methods are still competitive synthesizers. State-of-the-art statistical methods consistently outperform all deep generative synthesizers when DP is required, especially when the privacy budget is small (e.g., ε = 0.5)." "Large language models (LLM) are semantic-aware synthesizers. LLM-based method (i.e., GReaT) excels at generating realistic tabular data when the input dataset consists of rich semantic attributes, establishing a new paradigm in data synthesis."

Key Insights Distilled From

by Yuntao Du,Ni... at 04-16-2024
Systematic Assessment of Tabular Data Synthesis Algorithms

Deeper Inquiries

How can the proposed evaluation framework be extended to handle more complex data types beyond tabular data, such as time series, graph-structured, or multimedia data

The proposed evaluation framework for tabular data synthesis can be extended to handle more complex data types by adapting the evaluation metrics and methodologies to suit the specific characteristics of the data. For time series data, additional metrics such as temporal consistency and trend preservation can be incorporated into the fidelity evaluation. Privacy evaluation for time series data may involve considering the sequential nature of the data and the impact of individual data points on the overall privacy risk. Utility assessment for time series data could focus on the ability of the synthetic data to capture seasonality, trends, and other time-dependent patterns. For graph-structured data, fidelity evaluation could include measures of structural similarity and node/edge preservation. Privacy evaluation may involve analyzing the impact of node/link disclosure on the overall graph privacy. Utility assessment for graph data could focus on the ability of the synthetic data to preserve important graph properties such as connectivity, centrality, and community structure. For multimedia data, fidelity evaluation could involve metrics related to visual or auditory similarity between real and synthetic data. Privacy evaluation for multimedia data may consider the impact of pixel-level disclosure on privacy risks. Utility assessment for multimedia data could focus on the ability of the synthetic data to retain important features and characteristics of the original multimedia content. In all cases, the evaluation framework would need to be adapted to accommodate the specific characteristics and requirements of the data type, ensuring that the metrics used are relevant and meaningful for assessing fidelity, privacy, and utility in the context of the specific data domain.

What are the potential trade-offs between the three evaluation dimensions (fidelity, privacy, and utility) when designing new synthesis algorithms, and how can these trade-offs be optimized

When designing new synthesis algorithms, there are potential trade-offs between fidelity, privacy, and utility that need to be considered. Optimizing these trade-offs involves finding a balance that meets the requirements of the specific application or data domain. Fidelity: Increasing fidelity typically involves capturing more details and nuances of the original data distribution, which can enhance the quality of the synthetic data. However, this may come at the cost of increased computational complexity and potential privacy risks if sensitive information is preserved too closely. Privacy: Ensuring privacy often involves introducing noise or perturbations to the data to prevent re-identification of individuals. This can lead to a loss of fidelity and utility as the synthetic data may deviate from the original distribution. Balancing privacy with fidelity and utility is crucial to prevent privacy breaches while maintaining the usefulness of the synthetic data. Utility: Maximizing utility involves ensuring that the synthetic data is effective for downstream tasks such as machine learning or analysis. This may require preserving key statistical properties and relationships present in the original data. However, enhancing utility could potentially compromise privacy if too much information is retained. To optimize these trade-offs, synthesis algorithms can be fine-tuned based on the specific requirements of the application or data domain. This may involve adjusting hyperparameters, selecting appropriate models, or incorporating domain-specific constraints to achieve the desired balance between fidelity, privacy, and utility. Additionally, a systematic evaluation framework, like the one proposed, can help in quantifying and comparing these trade-offs across different algorithms and datasets to inform the decision-making process.

Given the varying performance of different synthesis algorithms on different datasets, how can the selection of the most appropriate synthesizer be automated or guided for a specific application or data domain

Automating or guiding the selection of the most appropriate synthesizer for a specific application or data domain can be achieved through a systematic approach that takes into account the unique characteristics and requirements of the data. Here are some strategies to automate or guide the selection process: Feature-Based Selection: Develop a feature-based approach to match the characteristics of the data with the capabilities of the synthesis algorithms. Define a set of features that describe the data complexity, structure, and distribution, and use these features to recommend suitable synthesizers based on their performance on similar datasets. Machine Learning Models: Train machine learning models to predict the performance of different synthesis algorithms on specific datasets. Use historical data on algorithm performance and dataset characteristics to build predictive models that can suggest the most appropriate synthesizer for a given dataset. Hyperparameter Optimization: Implement automated hyperparameter optimization techniques to fine-tune synthesis algorithms for specific datasets. Use tools like Bayesian optimization or grid search to search for the optimal hyperparameters that maximize performance metrics like fidelity, privacy, and utility. Ensemble Approaches: Combine multiple synthesis algorithms in an ensemble approach to leverage the strengths of each algorithm for different aspects of the data. Develop a framework that dynamically selects the best synthesizer or combination of synthesizers based on the specific requirements of the task. Feedback Loop: Implement a feedback loop mechanism that continuously evaluates the performance of the selected synthesizer on the target task and dataset. Use the feedback to iteratively improve the selection process and adapt to changing data characteristics or requirements. By incorporating these strategies into the selection process, practitioners can automate or guide the choice of the most suitable synthesizer for a specific application or data domain, ensuring optimal performance and alignment with the desired outcomes.