Authenticating q-Gram-Based Similarity Search Results for Outsourced String Databases

Abstract

Approximate string searches have been widely applied in many fields, such as bioinformatics, text retrieval, search engines, and location-based services (LBS). However, the approximate string search results from third-party servers may be incorrect due to the possibility of malicious third parties or compromised servers. In this paper, we design an authenticated index structure (AIS) for string databases, which is based on the Merkle hash tree (MHT) method and the q-gram inverted index. Our AIS can facilitate verification object (VO) construction for approximate string searches with edit distance thresholds. We design an efficient algorithm named GS2 for VO construction at the server side and search result verification at the user side. We also introduce an optimization method called GS2-opt that can reduce VO size dramatically. Finally, we conduct extensive experiments on real datasets to show that our proposed methods are efficient and promising.