VLDB Endowment, Trondheim, Norway, 901-909. On k-anonymity and the curse of dimensionality.
Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.
Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. To this end, we explain and discuss a selection of over 80 privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In this survey, we alleviate these problems by structuring the landscape of privacy metrics. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. The diversity and complexity of privacy metrics in the literature make an informed choice of metrics challenging. In this way, privacy metrics contribute to improving user privacy in the digital world. The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies.
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