Privacy-Preserving Community-Aware Trending Topic Detection in Online Social Media - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2017

Privacy-Preserving Community-Aware Trending Topic Detection in Online Social Media

Theodore Georgiou
  • Function : Author
  • PersonId : 1026652
Amr El Abbadi
  • Function : Author
  • PersonId : 942093
Xifeng Yan
  • Function : Author
  • PersonId : 1026653

Abstract

Trending Topic Detection has been one of the most popular methods to summarize what happens in the real world through the analysis and summarization of social media content. However, as trending topic extraction algorithms become more sophisticated and report additional information like the characteristics of users that participate in a trend, significant and novel privacy issues arise. We introduce a statistical attack to infer sensitive attributes of Online Social Networks users that utilizes such reported community-aware trending topics. Additionally, we provide an algorithmic methodology that alters an existing community-aware trending topic algorithm so that it can preserve the privacy of the involved users while still reporting topics with a satisfactory level of utility.
Fichier principal
Vignette du fichier
453481_1_En_11_Chapter.pdf (551.31 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01684372 , version 1 (15-01-2018)

Licence

Attribution

Identifiers

Cite

Theodore Georgiou, Amr El Abbadi, Xifeng Yan. Privacy-Preserving Community-Aware Trending Topic Detection in Online Social Media. 31th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC), Jul 2017, Philadelphia, PA, United States. pp.205-224, ⟨10.1007/978-3-319-61176-1_11⟩. ⟨hal-01684372⟩
98 View
79 Download

Altmetric

Share

Gmail Facebook X LinkedIn More