Using Metrics Suites to Improve the Measurement of Privacy in Graphs

Date

2020-03-13

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE Computer Society

Type

Article

Peer reviewed

Yes

Abstract

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.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

graph anonymization, graph de-anonymization, privacy, privacy metrics, monotonicity, metrics suites

Citation

Zhao, Y. and Wagner, I. (2020) Using Metrics Suites to Improve the Measurement of Privacy in Graphs. IEEE Transactions on Dependable and Secure Computing.

Rights

Research Institute

Cyber Technology Institute (CTI)