Protecting privacy in the age of big data: exploring data linking methods for quasi-identifier selection

HIGHLIGHTS

• What: The authors explore recent advancements in identifying semantically related datasets across heterogeneous data sources. In light of these challenges, the authors propose complementing heuristic-based methods by also considering the actual external availability of the data as empirical evidence for the identification of an attribute as QID. With this empirical layer, the authors aim to avoid the over-privatization of attributes that are not widely available to potential attackers, and the under-privatization of those that are readily accessible. The authors explore the use of empirical evidence to enhance the accuracy of re-identification risk scores . . .
  

   

  

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