Technical Privacy Metrics: a Systematic Survey

Abstract

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.

References in corpus (5)

• Deep Learning with Differential Privacy
• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
• Constructing elastic distinguishability metrics for location privacy
• From t-closeness to differential privacy and vice versa in data anonymization
• Evaluating the Strength of Genomic Privacy Metrics

Cited by in corpus (18)

• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
• More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
• The Long Road to Computational Location Privacy: A Survey
• Data Minimization for GDPR Compliance in Machine Learning Models
• A Survey of Privacy Vulnerabilities of Mobile Device Sensors
• Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
• Privacy and Confidentiality in Process Mining -- Threats and Research Challenges
• Normative Challenges of Risk Regulation of Artificial Intelligence and Automated Decision-Making
• Privacy-preserving Voice Analysis via Disentangled Representations
• Privacy Risk Assessment: From Art to Science, By Metrics
• Data Readiness for AI: A 360-Degree Survey
• Pointwise Maximal Leakage
• Synthetic Data: Revisiting the Privacy-Utility Trade-off
• Distributed Hypothesis Testing with Privacy Constraints
• Using Metrics Suites to Improve the Measurement of Privacy in Graphs
• Extremal Mechanisms for Pointwise Maximal Leakage
• DUEF-GA: Data Utility and Privacy Evaluation Framework for Graph Anonymization
• Application-Oriented Selection of Privacy Enhancing Technologies