CRITPFDec 1, 2015

Technical Privacy Metrics: a Systematic Survey

arXiv:1512.00327v2191 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the problem of informed metric choice for researchers and practitioners in privacy-enhancing technologies, offering a foundational framework to improve comparability and reduce redundant metric proposals.

The authors tackled the challenge of diverse and incomparable privacy metrics in the literature by conducting a systematic survey that structures the landscape, categorizing over eighty metrics and providing a method for selection based on nine questions.

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 makes 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 eighty 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.

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