{"type":"ScholarlyArticle","title":"FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data","authors":[{"name":"Haipeng Chen","slug":"haipeng-chen","given":"Haipeng","family":"Chen","affiliation":"Dartmouth College"},{"name":"Sushil Jajodia","slug":"sushil-jajodia","given":"Sushil","family":"Jajodia","affiliation":"George Mason University"},{"name":"Jing Liu","slug":"jing-liu","given":"Jing","family":"Liu","affiliation":"George Mason University"},{"name":"Noseong Park","slug":"noseong-park","given":"Noseong","family":"Park","affiliation":"George Mason University"},{"name":"Vadim Sokolov","slug":"vadim-sokolov","given":"Vadim","family":"Sokolov","affiliation":"George Mason University"},{"name":"V. S. Subrahmanian","slug":"v-s-subrahmanian","given":"V. S.","family":"Subrahmanian","affiliation":"Dartmouth College"}],"abstract":"In many cases, an organization wishes to release some data, but is restricted in the amount of data to be released due to legal, privacy and other concerns. For instance, the US Census Bureau releases only 1% of its table of records every year, along with statistics about the entire table. However, the machine learning (ML) models trained on the released sub-table are usually sub-optimal. In this paper, our goal is to find a way to augment the sub-table by generating a synthetic table from the released sub-table, under the constraints that the generated synthetic table (i) has similar statistics as the entire table, and (ii) preserves the functional dependencies of the released sub-table. We propose a novel generative adversarial network framework called ITS-GAN, where both the generator and the discriminator are specifically designed to satisfy these two constraints. By evaluating the augmentation performance of ITS-GAN on two representative datasets, the US Census Bureau data and US Bureau of Transportation Statistics (BTS) data, we show that ITS-GAN yields high quality classification results, and significantly outperforms various state-of-the-art data augmentation approaches.","doi":"10.24963/ijcai.2019/287","url":"https://ijcai.org/proceedings/2019/287","pdf":"https://ijcai.org/proceedings/2019/0287.pdf","markdown":"https://ijcai.org/proceedings/2019/287.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/287","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":287,"pages":{"first":2074,"last":2080},"keywords":["Machine Learning: Learning Generative Models","Machine Learning Applications: Applications of Supervised Learning","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Yu, 2004] Charu C. 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