Litcius/Paper detail

Secure Multi-keyword Fuzzy Searches With Enhanced Service Quality in Cloud Computing

Qin Liu, Yu Peng, Jie Wu, Tian Wang, Guojun Wang

2020IEEE Transactions on Network and Service Management79 citationsDOI

Abstract

With the ever-increasing amount of data resided in a cloud, how to provide users with secure and practical query services has become the key to improve the quality of cloud services. Fuzzy searchable encryption (FSE) is identified as one of the most promising approaches for enabling secure query services, since it allows searching encrypted data by using keywords with spelling errors. However, existing FSE schemes are far from the practical use for the following reasons: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inflexibility.</i> It is hard for them to simultaneously support AND and OR semantics in a multi-keyword query. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inefficiency.</i> They require sequentially scanning a whole dataset to find matched files, and thus are difficult to apply to a large-scale dataset. (3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Limited robustness.</i> It is difficult for them to resist the linear analysis attack in the known-background model. To fix the above problems, this article proposes matrix-based multi-keyword fuzzy search (M2FS) schemes, which support approximate keyword matching by exploiting the indecomposable property of primes. Specifically, we first present a basic scheme, called M2FS-B, where multiple keywords in a query or a file are constructed as prime-related matrices such that the result of matrix multiplication can be employed to determine the level of matching for different query semantics. Then, we construct an advanced scheme, named M2FS-E, which builds a searchable index as a keyword balanced binary (KBB) tree for dynamic and parallel searches, while adding random noises into a query matrix for enhanced robustness. Extensive analyses and experiments demonstrate the validity of our M2FS schemes.

Topics & Concepts

Computer scienceCloud computingEncryptionInformation retrievalCryptographyRobustness (evolution)Data miningApproximate string matchingTheoretical computer scienceDatabaseAlgorithmArtificial intelligenceComputer securityPattern matchingBiochemistryChemistryGeneOperating systemCryptography and Data SecurityComplexity and Algorithms in GraphsCloud Data Security Solutions
Secure Multi-keyword Fuzzy Searches With Enhanced Service Quality in Cloud Computing | Litcius