Matching Anonymized Individuals with Errors for Service Systems

EasyChair Preprint 1297

Abstract

Data privacy is of great importance for the healthy development of service systems. Companies and governments that provide services to people often have big concerns in sharing their data. Because of that, data must be pre-processed (e.g., anonymized) before they can be shared. However, without identification, it is difficult to match data from different sources and thus the data cannot be used together. This paper investigates how the performance of two simple individual matching methods was affected by errors in the similarity scores between individuals. The first method is a greedy method (GM) that simply matches individuals based on the maximum similarity scores. The second method is an optimal assignment problem (AP), which maximizes the total similarity scores of the matched individuals. Consistent with the literature, we found that GM outperforms AP in most situations. However, we also discovered that AP could be better in fixing errors.

Keyphrases: data correlation, data matching, service systems

@booklet{EasyChair:1297,
  author    = {Wai Kin Victor Chan},
  title     = {Matching Anonymized Individuals with Errors for Service Systems},
  howpublished = {EasyChair Preprint 1297},
  year      = {EasyChair, 2019}}