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Matching Anonymized Individuals with Errors for Service Systems

In: Smart Service Systems, Operations Management, and Analytics

Author

Listed:
  • Wai Kin (Victor) Chan

    (Tsinghua-Berkeley Shenzhen Institute, Tsinghua University)

Abstract

Data privacy is of great importance for the healthy development of service systemsService systems . Companies and governments that provide services to people often have big concerns in sharing their data. Because of that, data must be preprocessed (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.

Suggested Citation

  • Wai Kin (Victor) Chan, 2020. "Matching Anonymized Individuals with Errors for Service Systems," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), Smart Service Systems, Operations Management, and Analytics, pages 161-168, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-30967-1_15
    DOI: 10.1007/978-3-030-30967-1_15
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