We propose MarGinal Difference (MGD), a utility metric for private data publication. MGDassigns a difference score between the synthesized dataset and the ground truth dataset. The high level idea behind MGD is to measure the differences between many pairs marginal tables, each pair having one computed from the two datasets. For measuring the difference between a pair of marginal tables, we introduce Approximate Earth Mover Cost, which considers both semantic meanings of attribute values and the noisy nature of the synthesized dataset.
Prize amount: $5,000
Team members: Ninghui Li, Trung Đặng Đoàn Đức, Zitao Li, Tianhao Wang