Prime Inner Product Encoding for Effective Wildcard-Based Multi-Keyword Fuzzy Search

• Published in: IEEE transactions on services computing Vol. 15; no. 4; pp. 1799 - 1812
• Main Authors: Liu, Qin, Peng, Yu, Pei, Shuyu, Wu, Jie, Peng, Tao, Wang, Guojun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Data encryption; Data outsourcing; Data privacy; Encryption; fuzzy search; Indexes; Keywords; Prime numbers; primes; Privacy; Queries; searchable encryption; Searching; Semantics; wildcard
• ISSN: 1939-1374; 2372-0204
• DOI: 10.1109/TSC.2020.3020688

With the prevalence of cloud computing, a growing number of users are delegating clouds to host their sensitive data. To preserve user privacy, it is suggested that data is encrypted before outsourcing. However, data encryption makes keyword-based searches over ciphertexts extremely difficult. This is even challenging for fuzzy search that allows uncertainties or misspellings of keywords in a query. In this article, we propose a prime inner product encoding (PIPE) scheme, which makes use of the indecomposable property of prime numbers to provide efficient, highly accurate, and flexible multi-keyword fuzzy search. Our main idea is to encode either a query keyword or an index keyword into a vector filled with primes or reciprocals of primes, such that the result of vectors' inner product is an integer only when two keywords are similar. Specifically, we first construct <inline-formula><tex-math notation="LaTeX">\text{PIPE}_{0}</tex-math> <mml:math><mml:msub><mml:mtext>PIPE</mml:mtext><mml:mn>0</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="liu-ieq1-3020688.gif"/> </inline-formula> that is secure in the known ciphertext model. Unlike existing works that have difficulty supporting AND and OR semantics simultaneously, <inline-formula><tex-math notation="LaTeX">\text{PIPE}_{0}</tex-math> <mml:math><mml:msub><mml:mtext>PIPE</mml:mtext><mml:mn>0</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="liu-ieq2-3020688.gif"/> </inline-formula> gives users the flexibility to specify different search semantics in their queries. Then, we construct <inline-formula><tex-math notation="LaTeX">\text{PIPE}_{\text{S}}</tex-math> <mml:math><mml:msub><mml:mtext>PIPE</mml:mtext><mml:mtext>S</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="liu-ieq3-3020688.gif"/> </inline-formula> that subtly adds random noises to a query vector to resist linear analyses. Both theoretical analyses and experiment results demonstrate the effectiveness of our scheme.