Verification of User-Reported Context Claims with Context Correlation Model

Context-aware services nowadays offer incentive to user-reported context information , which inevitably solicits malicious users to cheat by submitting fabricated context claims. Conventional countermeasures based on Trusted Computing Base typically focus on particular context of interest, while disregarding the availability of various types of context information and the intrinsic correlation among them. In this work we propose a context claim verification scheme that interrogates correlated contexts of multiple dimensions to corroborate or contradict the reported context. Specifically, it first learns and models the context correlation with a Bayesian Multinet. Given a claim consisting of reported context and witnessing evidence, the scheme performs Bayesian inference with the evidence to verify the reported context. The verification process is light-weight, and can be applied to arbitrary types of context with a single model learnt. Evaluations on Reality Mining dataset and synthetic dataset validates choice of Multinet for data modeling, and demonstrate the feasibility of our scheme in context verification.