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Privacy Protected Identification of User Clusters in Large Organizations based on Anonymized Mattermost User and Channel Information

Authors:
Igor Jakovljevic
Martin Pobaschnig
Christian Gütl
Andreas Wagner

Keywords: Data Privacy; Open Data; Large Organizations; Clustering.

Abstract:
Oversharing exposes risks such as improved targeted advertising and sensitive information leakage. Requiring only the bare minimum of data diminishes these risk factors while simultaneously increasing the privacy of each individual user. Using anonymized data for finding communities enables new possibilities for large organizations under strong data protection regulations. While related work often focuses on privacy-preserving community detection algorithms including differential privacy, in this paper, the focus was set on the anonymized data itself. Channel membership information was used to build a weighted social graph, and groups of interest were identified using popular community detection algorithms. Graphs based on channel membership data satisfactorily resembled interest groups within the network but failed to capture the organizational structure.

Pages: 62 to 67

Copyright: Copyright (c) IARIA, 2022

Publication date: November 13, 2022

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-994-2

Location: Valencia, Spain

Dates: from November 13, 2022 to November 17, 2022