{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:39:21Z","timestamp":1772933961661,"version":"3.50.1"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,8]]},"DOI":"10.1109\/bigdata66926.2025.11402338","type":"proceedings-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:57:57Z","timestamp":1772830677000},"page":"3132-3141","source":"Crossref","is-referenced-by-count":0,"title":["SHAP Distance: An Explainability-Aware Metric for Evaluating the Semantic Fidelity of Synthetic Tabular Data"],"prefix":"10.1109","author":[{"given":"Ke","family":"Yu","sequence":"first","affiliation":[{"name":"The University of Tokyo,Tokyo,Japan"}]},{"given":"Shigeru","family":"Ishikura","sequence":"additional","affiliation":[{"name":"Infomart Corporation,Tokyo,Japan"}]},{"given":"Yukari","family":"Usukura","sequence":"additional","affiliation":[{"name":"Infomart Corporation,Tokyo,Japan"}]},{"given":"Yuki","family":"Shigoku","sequence":"additional","affiliation":[{"name":"Infomart Corporation,Tokyo,Japan"}]},{"given":"Teruaki","family":"Hayashi","sequence":"additional","affiliation":[{"name":"The University of Tokyo,Tokyo,Japan"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Generative adversarial nets","author":"Goodfellow","year":"2014","journal-title":"NeurIPS"},{"key":"ref2","article-title":"Modeling tabular data using conditional GAN","author":"Xu","year":"2019","journal-title":"NeurIPS"},{"key":"ref3","first-page":"286305","article-title":"Generating Multi-label Discrete Patient Records using Generative Adversarial Networks","author":"Choi","year":"2017","journal-title":"MLHC"},{"key":"ref4","article-title":"TabDDPM: Modeling tabular data with diffusion models","author":"Kotelnikov","year":"2023","journal-title":"ICML"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-020-00977-1"},{"key":"ref6","volume-title":"sdv-dev\/SDMetrics: v0.23.0","year":"2025"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref8","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"NeurIPS"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8080832"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588573"},{"key":"ref11","article-title":"PATE-GAN: Generating synthetic data with differential privacy","author":"Jordon","year":"2019","journal-title":"ICLR"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/INOCON57975.2023.10101315"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3419394.3423643"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.14778\/3231751.3231757"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2016.49"},{"key":"ref16","article-title":"Language models are few-shot learners","author":"Brown","year":"2020","journal-title":"NeurIPS"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3233\/shti240571"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-08328-6"},{"key":"ref19","article-title":"Benchmarking synthetic tabular data: A multi-dimensional evaluation framework","author":"Sidorenko","year":"2025","journal-title":"arXiv preprint"},{"key":"ref20","article-title":"Synthcity: a benchmark framework for diverse use cases of tabular synthetic data","author":"Qian","year":"2023","journal-title":"NeurIPS"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1466"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/tai.2022.3229289"},{"key":"ref23","article-title":"A correlation- and meanaware loss function and benchmarking framework to improve GANbased tabular data synthesis","volume-title":"arXiv preprint","author":"Vu","year":"2024"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1055\/s-0042-1760247"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02731-9"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105413"},{"key":"ref27","article-title":"Axiomatic attribution for deep networks","author":"Sundararajan","year":"2017","journal-title":"ICML"},{"key":"ref28","article-title":"Learning important features through propagating activation differences","author":"Shrikumar","year":"2017","journal-title":"ICML"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3063289"},{"key":"ref31","article-title":"Sanity checks for saliency maps","author":"Adebayo","year":"2018","journal-title":"NeurIPS"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13193806"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109772"},{"key":"ref35","article-title":"Explainability in action: A metric-driven assessment of five XAI methods for healthcare tabular models","author":"Qureshi","year":"2025","journal-title":"medRxiv 2025.05.20.25327976"},{"key":"ref36","article-title":"Causality for tabular data synthesis: A high-order structure causal benchmark framework","volume-title":"arXiv preprint","author":"Tu","year":"2024"},{"key":"ref37","article-title":"KGSynX: Knowledge graph and explainable feedback guided LLMs for synthetic tabular data generation","author":"Yu","year":"2025","journal-title":"ISWC"}],"event":{"name":"2025 IEEE International Conference on Big Data (BigData)","location":"Macau, China","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,11]]}},"container-title":["2025 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11400704\/11400712\/11402338.pdf?arnumber=11402338","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T06:56:07Z","timestamp":1772866567000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11402338\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/bigdata66926.2025.11402338","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}