Quantifying and Measuring Anonymity

Murdoch, Steven J.

doi:10.1007/978-3-642-54568-9_1

Cited by 7 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the work that compares different privacy metrics and analyzes in which situations privacy metrics perform well is in anonymous communication. For example, Syverson examines entropy as a metric for anonymity and concludes that it should not be used in the context of anonymous communication [26], and Murdoch compares the strengths and weaknesses of several different metrics for anonymous communication [18].…”

Section: Criteria For Privacy Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Using Metrics Suites to Improve the Measurement of Privacy in Graphs

Zhao

Wagner

2022

IEEE Trans. Dependable and Secure Comput.

View full text Add to dashboard Cite

Social graphs are widely used in research (e.g., epidemiology) and business (e.g., recommender systems). However, sharing these graphs poses privacy risks because they contain sensitive information about individuals. Graph anonymization techniques aim to protect individual users in a graph, while graph de-anonymization aims to re-identify users. The effectiveness of anonymization and de-anonymization algorithms is usually evaluated with privacy metrics. However, it is unclear how strong existing privacy metrics are when they are used in graph privacy. In this paper, we study 26 privacy metrics for graph anonymization and de-anonymization and evaluate their strength in terms of three criteria: monotonicity indicates whether the metric indicates lower privacy for stronger adversaries; for within-scenario comparisons, evenness indicates whether metric values are spread evenly; and for between-scenario comparisons, shared value range indicates whether metrics use a consistent value range across scenarios. Our extensive experiments indicate that no single metric fulfills all three criteria perfectly. We therefore use methods from multi-criteria decision analysis to aggregate multiple metrics in a metrics suite, and we show that these metrics suites improve monotonicity compared to the best individual metric. This important result enables more monotonic, and thus more accurate, evaluations of new graph anonymization and de-anonymization algorithms.

show abstract

Section: Criteria For Privacy Metricsmentioning

confidence: 99%

“…One reason why entropy and metrics derived from entropy have low monotonicity scores may be that low-probability candidates have a large influence on entropy [4], [18]. To find out whether this is the case here, we plot the adversary's probability distribution using a histogram with 100 bins (Figure 5a).…”

Section: Entropy Affected By Low-probability Candidatesmentioning

confidence: 99%

Using Metrics Suites to Improve the Measurement of Privacy in Graphs

Zhao

Wagner

2022

IEEE Trans. Dependable and Secure Comput.

View full text Add to dashboard Cite

show abstract

“…However, despite the large number of privacy metrics and the existence of criteria for privacy metrics, we are not aware of systematic efforts to evaluate the strength of privacy metrics for vehicular communications. The most closely related work in this respect is Murdoch's evaluation of metrics for anonymous communication [16].…”

Section: Evaluation Of Privacy Metricsmentioning

confidence: 99%

On the Strength of Privacy Metrics for Vehicular Communication

Zhao

Wagner

2019

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Vehicular communication plays a key role in near-future automotive transport, promising features such as increased traffic safety and wireless software updates. However, vehicular communication can expose drivers' locations and thus poses privacy risks. Many schemes have been proposed to protect privacy in vehicular communication, and their effectiveness is usually evaluated with privacy metrics. However, to the best of our knowledge, (1) different privacy metrics have never been compared to each other, and (2) it is unknown how strong the metrics are. In this paper, we evaluate and compare the strength of 41 privacy metrics in terms of four novel criteria: Privacy metrics should be monotonic, i.e., indicate decreasing privacy for increasing adversary strength; their values should be spread evenly over a large value range to support within-scenario comparability; and they should share a large portion of their value range between traffic conditions to support between-scenario comparability. We evaluate all four criteria on real and synthetic traffic with state-of-the-art adversary models and create a ranking of privacy metrics. Our results indicate that no single metric dominates across all criteria and traffic conditions. We therefore recommend to use metrics suites, i.e., combinations of privacy metrics, when evaluating new privacy-enhancing technologies.

show abstract

Weighted Factors for Evaluating Anonymity

Shahbar

Zincir-Heywood

2018

Foundations and Practice of Security

View full text Add to dashboard Cite

Quantifying and Measuring Anonymity

Cited by 7 publications

References 7 publications

Using Metrics Suites to Improve the Measurement of Privacy in Graphs

Using Metrics Suites to Improve the Measurement of Privacy in Graphs

On the Strength of Privacy Metrics for Vehicular Communication

Weighted Factors for Evaluating Anonymity

Contact Info

Product

Resources

About