Techniques to hide a community from community detection algorithms are emerging as a new way to protect the privacy of users. Existing techniques either adapt optimization criteria derived from community detection (e.g., minimizing instead of maximizing modularity) or define new ones (e.g., community safeness) to identify a set of updates (e.g., edge addition/deletions) that deceive community detection algorithms from recovering the original structure of a target community C. However, all existing approaches do not take into account the fact that network’s edges can be weighted to take into account node similarity of relation strength. The goal of this paper is to present Secretorum, a novel community deception approach for community deception in weighted networks.

Community Deception in Weighted Networks / Fionda, V.; Pirro', G.. - (2021). ((Intervento presentato al convegno 2021 IEEE/ACM conference on Advances in Social Network Analysis and Mining (ASONAM) tenutosi a Virtuale.

Community Deception in Weighted Networks

G. Pirro'
2021

Abstract

Techniques to hide a community from community detection algorithms are emerging as a new way to protect the privacy of users. Existing techniques either adapt optimization criteria derived from community detection (e.g., minimizing instead of maximizing modularity) or define new ones (e.g., community safeness) to identify a set of updates (e.g., edge addition/deletions) that deceive community detection algorithms from recovering the original structure of a target community C. However, all existing approaches do not take into account the fact that network’s edges can be weighted to take into account node similarity of relation strength. The goal of this paper is to present Secretorum, a novel community deception approach for community deception in weighted networks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1574072
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