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MGD: A Utility Metric for Private Data Publication

Project Summary

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.

Place: 1st

Prize amount: $5,000

Team members: Ninghui Li, Trung Đặng Đoàn Đức, Zitao Li, Tianhao Wang

Created February 16, 2021