Nanayakkara, ChariniChristen, PeterRanbaduge, ThilinaD'Aquin, MatthieuDietze, Stefan2023-07-202023-07-20October 19978-1-4503-6859-9http://hdl.handle.net/1885/294457Analysis of graph data is extensively conducted in numerous domains to learn the relationships between and behaviour of connected entities. Many graphs contain sensitive data, for example social network users and their posts, or genealogical records such as birth and death certificates. This has limited the use and publication of such sensitive graph data sets. While there are various techniques available to anonymise tabular data, anonymising graph data while maintaining the node and edge structure of the original graph, such as node attributes and the similarities between nodes, is a challenging task. In this paper, we present a web tool which can anonymise sensitive graph data while maintaining the similarity structure of the original graph by employing a cluster-based mapping of sensitive to public attribute values, as well as randomly shifting date values. Our demonstration will illustrate the tool on two example data sets of historical birth records.This research was partially funded by the Australian Research Council under grant DP160101934application/pdfen-AU© 2020 Copyright for this paper by its authors.https://creativecommons.org/licenses/by/4.0/Graph anonymisationsensitive datadata privacydata generationcluster mappingstring similarity20202024-01-21Creative Commons Attribution 4.0 International License