Recent research has highlighted the importance of maintaining user privacy on online social networks. Specifically, despite their appealing applications, community detection algorithms expose personal relationships that might be misused against social network members. This concern has opened a new research area called community deception, which is about hiding the members of a target community from community detection algorithms. State-of-the-art deception methods only look at how community members must perform edge updates to guarantee some level of hiding. This article introduces node-centric deception, a novel approach considering nodes entering and leaving a target community. We theoretically study the effect of node updates by leveraging node safeness as a deception optimization function. Based on this analysis, we present an effective heuristic capable of hiding the target community with minimal node operations. We evaluated our approach against several community detection algorithms and compared it with state-of-the-art deception algorithms with encouraging results.
Node-Centric Community Deception Based on Safeness
Pirro' G.
2023-01-01
Abstract
Recent research has highlighted the importance of maintaining user privacy on online social networks. Specifically, despite their appealing applications, community detection algorithms expose personal relationships that might be misused against social network members. This concern has opened a new research area called community deception, which is about hiding the members of a target community from community detection algorithms. State-of-the-art deception methods only look at how community members must perform edge updates to guarantee some level of hiding. This article introduces node-centric deception, a novel approach considering nodes entering and leaving a target community. We theoretically study the effect of node updates by leveraging node safeness as a deception optimization function. Based on this analysis, we present an effective heuristic capable of hiding the target community with minimal node operations. We evaluated our approach against several community detection algorithms and compared it with state-of-the-art deception algorithms with encouraging results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.