Technical Privacy Metrics

By Isabel Wagner, David Eckhoff. 2015

196 citations recorded by the source corpus.

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. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature make an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey, we alleviate these problems by structuring the landscape of privacy metrics. 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 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. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Federated Learning: Challenges, Methods, and Future Directions (2019, background): “r privacy in machine learning, there are many other privacy deﬁnitions, such as k-anonymity [36], d-presence [81] and distance correlation [117], that may be applicable to different learning problems [118]. 10 !"#$"# %"$&'"! (a) Federated learning without additional priv”