We propose a methodology to systematically evaluate the monotonicity of graph privacy metrics, and present preliminary results for the monotonicity of 25 graph ...
POSTER: Evaluating Privacy Metrics for Graph Anonymization and De-anonymization. Yuchen Zhao. De Montfort University. Leicester, UK [email protected].
A methodology to systematically evaluate the monotonicity of graph privacy metrics is proposed, and preliminary results for the monotonicity of 25 graph ...
To protect graph privacy, data anonymization has been proposed to prevent individual users in a graph from being identified by adversaries. The effectiveness of ...
To protect graph privacy, data anonymization has been proposed to prevent individual users in a graph from being identified by adversaries. The effectiveness of ...
This article studies 26 privacy metrics for graph anonymization and de-anonymization and evaluates their strength in terms of three criteria: monotonicity ...
TL;DR: In this article , the authors study 26 privacy metrics for graph anonymization and de-anonymization and evaluate their strength in terms of three ...
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May 30, 2019 · In this paper, we study 26 privacy metrics for graph anonymization and de-anonymization and evaluate their strength in terms of three criteria.
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The result files are organized in three folders: metrics has the raw data for all privacy metrics, plots has box plots for all individual metrics as well as ...
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