Modeling data flow in socio-information networks: A risk estimation approach

Ting Wang, Mudhakar Srivatsa, Dakshi Agrawal, Ling Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Information leakage via the networks formed by subjects (e.g., Facebook, Twitter) and objects (e.g., blogosphere) - some of whom may be controlled by malicious insiders - often leads to unpredicted access control risks. While it may be impossible to precisely quantify information flows between two entities (e.g., two friends in a social network), this paper presents a first attempt towards leveraging recent advances in modeling socio-information networks to develop a statistical risk estimation paradigm for quantifying such insider threats. In the context of socio-information networks, our models estimate the following likelihoods: prior flow - has a subject s acquired covert access to object o via the networks? posterior flow - if s is granted access to o, what is its impact on information flows between subject s′ and object o!? network evolution - how will a newly created social relationship between s and s′ influence current risk estimates? Our goal is not to prescribe a one-size-fits-all solution; instead we develop a set of composable network-centric risk estimation operators, with implementations configurable to concrete socio-information networks. The efficacy of our solutions is empirically evaluated using real-life datasets collected from the IBM SmallBlue project and Twitter.

Original languageEnglish (US)
Title of host publicationSACMAT'11 - Proceedings of the 16th ACM Symposium on Access Control Models and Technologies
Pages113-122
Number of pages10
DOIs
StatePublished - Jul 15 2011
Event16th ACM Symposium on Access Control Models and Technologies, SACMAT 2011 - Innsbruck, Austria
Duration: Jun 15 2011Jun 17 2011

Publication series

NameProceedings of ACM Symposium on Access Control Models and Technologies, SACMAT

Conference

Conference16th ACM Symposium on Access Control Models and Technologies, SACMAT 2011
CountryAustria
CityInnsbruck
Period6/15/116/17/11

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality
  • Information Systems

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    Wang, T., Srivatsa, M., Agrawal, D., & Liu, L. (2011). Modeling data flow in socio-information networks: A risk estimation approach. In SACMAT'11 - Proceedings of the 16th ACM Symposium on Access Control Models and Technologies (pp. 113-122). (Proceedings of ACM Symposium on Access Control Models and Technologies, SACMAT). https://doi.org/10.1145/1998441.1998458