# Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics

arXiv: https://arxiv.org/abs/2401.06883 · HTML: https://mlsanity.com/paper/2401.06883/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [CTAB-GAN: Effective Table Data Synthesizing](https://mlsanity.com/paper/2102.08369/) (2021)
- [Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis](https://mlsanity.com/paper/1903.06753/) (2019)
- [Privacy-Preserving Synthetic Educational Data Generation](https://mlsanity.com/paper/2207.03202/) (2022)
- [UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation](https://mlsanity.com/paper/1810.06936/) (2018)
- [Synthetic Data Generation for Economists](https://mlsanity.com/paper/2011.01374/) (2020)
- [Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics](https://mlsanity.com/paper/2212.02622/) (2022)
- [A new PCA-based utility measure for synthetic data evaluation](https://mlsanity.com/paper/2212.05595/) (2022)

## Built on by (most cited first, 19 of 19)

- [HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection](https://mlsanity.com/paper/2408.02927/) (2024)
- [LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion](https://mlsanity.com/paper/2503.02161/) (2025)
- [Synthetic Data Privacy Metrics](https://mlsanity.com/paper/2501.03941/) (2025)
- [Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms](https://mlsanity.com/paper/2501.01785/) (2025)
- [Ensembling Membership Inference Attacks Against Tabular Generative Models](https://mlsanity.com/paper/2509.05350/) (2025)
- [Evaluating the Impact of Data Augmentation on Predictive Model Performance](https://mlsanity.com/paper/2412.02108/) (2024)
- [Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation](https://mlsanity.com/paper/2501.01793/) (2025)
- [Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning](https://mlsanity.com/paper/2503.13550/) (2025)
- [PuckTrick: A Library for Making Synthetic Data More Realistic](https://mlsanity.com/paper/2506.18499/) (2025)
- [DrawSim-PD: Simulating Student Science Drawings to Support NGSS-Aligned Teacher Diagnostic Reasoning](https://mlsanity.com/paper/2602.01578/) (2026)
- [Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints](https://mlsanity.com/paper/2505.05019/) (2025)
- [Boosting Predictive Performance on Tabular Data through Data Augmentation with Latent-Space Flow-Based Diffusion](https://mlsanity.com/paper/2511.16571/) (2025)
- [Measuring the Impact of Student Gaming Behaviors on Learner Modeling](https://mlsanity.com/paper/2512.18659/) (2025)
- [SecretFan: Synthesizing Realistic Data without Breaking Privacy](https://mlsanity.com/paper/2602.05833/) (2026)
- [Generating High-quality Privacy-preserving Synthetic Data](https://mlsanity.com/paper/2602.06390/) (2026)
- [Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation](https://mlsanity.com/paper/2602.09288/) (2026)
- [Autoregressive Synthesis of Sparse and Semi-Structured Mixed-Type Data](https://mlsanity.com/paper/2603.01444/) (2026)
- [Generating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning](https://mlsanity.com/paper/2605.26823/) (2026)
- [Not all Jensen-Shannon Divergence Estimators are Equal](https://mlsanity.com/paper/2606.16411/) (2026)

## Abstract (arXiv)

Privacy poses a significant obstacle to the progress of learning analytics (LA), presenting challenges like inadequate anonymization and data misuse that current solutions struggle to address. Synthetic data emerges as a potential remedy, offering robust privacy protection. However, prior LA research on synthetic data lacks thorough evaluation, essential for assessing the delicate balance between privacy and data utility. Synthetic data must not only enhance privacy but also remain practical for data analytics. Moreover, diverse LA scenarios come with varying privacy and utility needs, making the selection of an appropriate synthetic data approach a pressing challenge. To address these gaps, we propose a comprehensive evaluation of synthetic data, which encompasses three dimensions of synthetic data quality, namely resemblance, utility, and privacy. We apply this evaluation to three distinct LA datasets, using three different synthetic data generation methods. Our results show that synthetic data can maintain similar utility (i.e., predictive performance) as real data, while preserving privacy. Furthermore, considering different privacy and data utility requirements in different LA scenarios, we make customized recommendations for synthetic data generation. This paper not only presents a comprehensive evaluation of synthetic data but also illustrates its potential in mitigating privacy concerns within the field of LA, thus contributing to a wider application of synthetic data in LA and promoting a better practice for open science.