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

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...

• Type: Journal
• Year: 2011
• Where: SACMAT
• Authors: Ting Wang, Mudhakar Srivatsa, Dakshi Agrawal, Ling Liu
• Keywords: Computing And Information Systems, Control Systems, Information Systems Security, Risk Estimation, SACMAT 2011