Synthetic Data Evaluation

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Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis
Aug 30, 2026Jinmeng Li, Quan Zhang, Hangting Ye +4Synthetic Tabular Data Generation Privacy-Preserving ML
School of Artificial Intelligence, Jilin University, Changchun, China · Broad College of Business, Michigan State University, East Lansing, USA · Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia +2
LOB-ID: Evaluating Synthetic Market Data by Inception Distances
Aug 13, 2026Andreea Bacalum, Zhuohan Wang, Ollie Olby +2Quantitative Finance Synthetic Data Evaluation
Simudyne · King's College London, United Kingdom
CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility
Aug 13, 2026Akanta Das, Al Amin Farhad, Mrinmoy Sarkar Anto +3Benchmark Design Synthetic Data Generation
Bangladesh University of Engineering and Technology, Dhaka, Bangladesh · School of Medicine, Stanford University, Stanford, California, United States
Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting
Aug 11, 2026Luis Amorim, Vitor Cerqueira, Moises Santos +2Synthetic Data Generation Time Series Forecasting
Faculdade de Engenharia da Universidade do Porto, Porto, Portugal · University of Coimbra, Coimbra, Portugal · Laboratory for Artificial Intelligence and Computer Science (LIACC), Portugal +3
Compositional Benchmark Synthesis for Hierarchical Human Action Recognition
Aug 11, 2026Farnaz Soleimani, Abdelghani Chibani, Yacine Amirat +1Human Activity Recognition Synthetic Benchmark Generation
LISSI
Privacy-Preserving Data Drift Detection and Recovery for Large-Scale LLM Applications via Proxy Representations
Aug 8, 2026Michael Levit, Josh Ledgard, Haoyu Dong +5LLM Evaluation Synthetic Data Generation
Microsoft Corporation
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
Aug 6, 2026Omid Bazgir, Md Nasir, Jacob Hoffman +6Synthetic Benchmark Generation Benchmark Validity
Oracle Health and Life Sciences United States
Assessment of Conditional Diffusion Model for Synthetic Histopathology Image Generation
Aug 4, 2026Seyed Kahaki, Shijie Li, Weijie Chen +1Medical Image Synthesis Image Generation Evaluation
Division of Imaging, Diagnostics, and Software Reliability, U.S. Food and Drug Administration (FDA)
Impact of Dataset Composition on Embedded Real-Time UAV Wildfire Detection Using Compact YOLO Models
Aug 1, 2026Eduardo de los Santos, Andre S. Kelbouscas, Ricardo B. Grando +1Synthetic Data Augmentation Wildfire Detection
Robotics and AI Lab, Technological University of Uruguay, Rivera, Uruguay
When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses
Jul 28, 2026Zihan Chen, Di Zhu, Lei Nico ZhengLLM Evaluation Large Language Model-Based Social Simulation
Stevens Institute of Technology Hoboken, NJ, USA · University of Massachusetts Boston Boston, MA, USA
On the Use of Synthetic Data for Threshold Calibration in Face Recognition: Performance and Security Implications for Border Control Systems
Jul 27, 2026Arto ApilaBiometric Identification Face Recognition
Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
Distributional Validity of a Korean Synthetic Persona Panel: Evidence From the Korea Media Panel Survey
Jul 23, 2026Howard Kim, Keuntae ChoPersona-Conditioned Generation Synthetic Data Evaluation
College of AI Convergence, Seoul Cyber University, Seoul 01133, South Korea · Department of Management of Technology, Graduate School, Sungkyunkwan University, Suwon 16419, South Korea · Department of Systems Management Engineering, Sungkyunkwan University, Suwon 16419, South Korea
Synthetic minority data is redundant or invalid: a data-dependent validity theory and a de-biased test
Jul 22, 2026Ahmad B. Hassanat, Ahmad S. Tarawneh, Ghada A. AltarawnehClass-Imbalanced Learning Synthetic Data Generation
Faculty of Information Technology, Mutah University, Karak, Jordan · Department of Accounting, Mutah University, Karak, Jordan
Measuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models
Jul 20, 2026Jie ZhangTwo-Sample Testing Synthetic Tabular Data Generation
Accenture, Tokyo 107-8672, Japan
Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data
Jul 17, 2026Kiwan Kwon, Kangmin Kim, Hojin Lee +5Synthetic Data Evaluation
UNIST · HUFS · JP Morgan AI Research +1
Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers
Jul 16, 2026Mahmoud Ibrahim, Bart Elen, Chang Sun +2Synthetic Data Pretraining Algorithmic Fairness
Institute of Data Science, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands · Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands · VITO, Belgium
Benchmarking Face Recognition without Real Faces
Jul 16, 2026Paweł Borsukiewicz, Daniele Lunghi, Wendkûuni C. Ouédraogo +2Privacy-Preserving ML Face Recognition
University of Luxembourg · Luxembourg
Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
Jul 14, 2026Martina Radoynova, Samuel Pantze, Trina De +2Synthetic-to-Real Domain Adaptation Synthetic Data Generation
Center for Advanced Systems Understanding (CASUS), Görlitz, Germany · Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR), Dresden, Germany · Institute of Computer Science, University of Wrocław, Wrocław, Poland +1
Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset
Jul 13, 2026Kuniko Paxton, Amila Akagić, Koorosh Aslansefat +2Synthetic Data Augmentation Synthetic Data Evaluation
School of Digital and Physical Science, University of Hull, Cottingham Road, Hull, HU6 7RX, United Kingdom · Faculty of Electrical Engineering, University of Sarajevo, Zmaja od Bosne bb, Sarajevo, 71000, Bosnia and Herzegovina
Trustworthy synthetic data for campaign decision support: strategy simulation fidelity and the PolicySynth framework
Jul 13, 2026Tung Dang, The Hung Phung, Son Lam Nguyen +1AI-Assisted Decision Making Synthetic Data Generation
aGraduate School of Science, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan · bFaculty of Human Resource Management, Trade Union University, 169 Tay Son, Kim Lien, Hanoi, Vietnam · cInstitute for Business Management Development, Academy Of Finance, 58 Le Van Hien Street, Dong Ngac Ward, Hanoi, Vietnam +1
AU-Guided Synthetic Video Generation for Micro-Expression Recognition
Jul 12, 2026Pei-Sze Tan, Sailaja Rajanala, Yee-Fan Tan +2Synthetic Data Generation Facial Expression Recognition
CyPhi AI Lab, Monash University Malaysia
Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data
Jul 8, 2026Vinícius Gabriel Angelozzi, Héber H. ArcoleziAlgorithmic Fairness Group Fairness
Centre Inria de l’Université Grenoble Alpes · ÉTS Montréal, Inria Grenoble
SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation
Jul 8, 2026Andrea Scarinci, Virginia Negri, Brayan Impata +3LLM-as-a-Judge LLM-Assisted Annotation
Amazon
Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain
Jul 7, 2026Aurora Francesca Zanenga, Andrea Bombarda, Marsha Chechik +4Synthetic Data Generation Automated Software Engineering
University of Bergamo Bergamo, Italy · University of Toronto Toronto, Canada · Humanitas Clinical and Research Center, IRCCS Rozzano, Italy +2
Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
Jul 7, 2026Riccardo Terrenzi, Serkan AyvazIR Evaluation Synthetic Data Evaluation
University of Southern Denmark Sønderborg, Denmark
Synthetic Consumer Insight Generation with Large Language Models
Jul 7, 2026Stephen L. France, Pia. A. AlbinssonLLM Prompting Synthetic Data Generation
Mississippi State University Mailstop 9582, Mississippi State, MS 39762
Fidelity-Diversity Metrics for Text
Jul 6, 2026Amanda Wang, Tudor Manole, Florentina Bunea +1Synthetic Data Evaluation
Department of Computer Science, Cornell University · Statistics and Data Science Center, Massachusetts Institute of Technology · Department of Statistics, Cornell University
Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series
Jul 5, 2026Zitao Shuai, Zongzhe Xu, Yuntian Wu +3Flow Matching Synthetic Data Generation
University of California, Los Angeles · 2Carnegie Mellon University · 3Massachusetts Institute of Technology
TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data
Jul 4, 2026Jialin Zhang, Fenghao Dong, Yajie Zhou +2Benchmark Design Synthetic Tabular Data Generation
†University of Hong Kong ¶Tongji University · ‡Carnegie Mellon University · §University of Maryland, College Park
Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence
Jul 1, 2026Jose Luis Vela Alonso, Carmen PellicerSynthetic Data Generation Network Intrusion Detection
CESTE, University Center, Zaragoza, Spain

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Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis

Jinmeng Li, Quan Zhang, Hangting Ye, He Zhao+3

School of Artificial Intelligence, Jilin University, Changchun, China · Broad College of Business, Michigan State University, East Lansing, USA · Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia+2

Synthetic Tabular Data Generation Privacy-Preserving ML Synthetic Data Evaluation

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Sharing tabular data in high-stakes domains is constrained by privacy regulations. Synthetic data offer a promising alternative, but deep generative models are costly to train and difficult to audit, while LLM-based methods often serialize records as text, obscuring tabular structure and exposing sensitive data. We introduce Tabular Synthesis Strategy Designer (TabSSD), which uses an LLM to design synthesis procedures rather than directly generate records. TabSSD provides the LLM with tree-derived summaries of variable dependence rather than raw records, which produces Python programs for local execution and evaluation. Across twelve datasets, TabSSD strikes a favourable balance among statistical fidelity, predictive utility, and empirical privacy risk, achieving the best average rank across six metrics among ten methods. Moreover, it substantially reduces local computation and token consumption relative to the compared methods. By enabling human-guided refinement and eliminating user-side model tuning, TabSSD lowers the expertise and infrastructure barriers to transparent tabular data synthesis.

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