%0 Conference Proceedings
%T Privacy-Preserving Community-Aware Trending Topic Detection in Online Social Media
%+ University of California [Los Angeles] (UCLA)
%A Georgiou, Theodore
%A El Abbadi, Amr
%A Yan, Xifeng
%Z Part 2: Privacy
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 31th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC)
%C Philadelphia, PA, United States
%Y Giovanni Livraga
%Y Sencun Zhu
%I Springer International Publishing
%3 Data and Applications Security and Privacy XXXI
%V LNCS-10359
%P 205-224
%8 2017-07-19
%D 2017
%R 10.1007/978-3-319-61176-1_11
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 11
%Z WG 11.3
%2 https://inria.hal.science/hal-01684372/document
%2 https://inria.hal.science/hal-01684372/file/453481_1_En_11_Chapter.pdf
%L hal-01684372
%U https://inria.hal.science/hal-01684372
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC11
%~ IFIP-WG11-3
%~ IFIP-DBSEC
%~ IFIP-LNCS-10359