Secure Semantic-Aware Search Over Dynamic Spatial Data in VANETs

Abstract

Vehicular Ad hoc Networks (VANETs) play an increasingly important role in a number of applications, particularly those associated with location-based services (e.g., spatial keyword searches – SKS). However, there is a need to strike a balance between privacy guarantee and search efficiency, and existing SKS solutions cannot be directly implemented in VANETs. In addition, existing schemes may also lack support for semantic-awareness in the dynamic setting. To address these limitations, we propose a Secure Semantic-aware Spatial Keyword Search scheme that supports Dynamic update (3SKSD). Specifically, we leverage the Latent Dirichlet Allocation (LDA) topic model and secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> Nearest Neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN) method to help us achieve both efficiency and security. We also construct an encrypted R-tree structure to facilitate SKS and dynamic update. Moreover, we propose an advanced scheme with forward security on the basis of 3SKSD, with the aim of minimizing privacy leakage due to dynamic updates. Our formal security analysis verifies the validity and security of 3SKSD, and findings from the experimental evaluation demonstrate its high search accuracy and efficiency.