MGD: A Utility Metric for Private Data Publication
Zitao Li, Trung Dang, Tianhao Wang, and Ninghui Li
In Proceedings of the 8th International Conference on Networking, Systems and Security, Cox’s Bazar, Bangladesh, 2021
Differential privacy has been accepted as one of the most popular techniques to protect user data privacy. A common way for utilizing private data under DP is to take an input dataset and synthesize a new dataset that preserves features of the input dataset while satisfying DP. A trade-off always exists between the strength of privacy protection and the utility of the final output: stronger privacy protection requires larger randomness, so the outputs usually have a larger variance and can be far from optimal. In this paper, we summarize our proposed metric for the NIST “A Better Meter Stick for Differential Privacy” competition [26], MarGinal Difference (MGD), for measuring the utility of a synthesized dataset. Our metric is based on earth mover distance. We introduce new features in our metric so that it is not affected by some small random noise that is unavoidable in the DP context but focuses more on the significant difference. We show that our metric can reflect the range query error better compared with other existing metrics. We introduce an efficient computation method based on the min-cost flow to alleviate the high computation cost of the earth mover’s distance.