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An enhanced wildcard-based fuzzy searching scheme in encrypted databases

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Abstract

Under the overwhelming trend in Cloud Computing, Cloud Databases possessing high scalability / high availability / high parallel performance have become a prevalent paradigm of data outsourcing. In consideration of security and privacy, both individuals and enterprises prefer to outsource service data in encrypted form. Unfortunately, most encrypted databases cannot support such complicated queries as wildcard-based fuzzy searching, which, to some extent, limits the practicability in real applications. To explore more business logic in encrypted databases, an enhanced wildcard-based fuzzy searching scheme (enWFS) is proposed in this paper, which integrates specialized Adjacent Character Matrix/Tensor into proxy middleware, appends two types of ancillary columns into data tables, as well as designs an advanced adaptive overwriting method to revise query expressions with wildcards (‘%’ and ‘_’). Meanwhile, some security enhancements and TupleRank are added to enWFS scheme so as to achieve superior fuzzy searching experiences. Extensive experiments based on real datasets demonstrate effectiveness, feasibility of our proposal.

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Notes

  1. For the sake of simplicity, among illustrations of this paper, the beginning and the end of a string will be uniformly represented as ‘#’ (as you can see in Table 1). But these two cases will be treated respectively during the implementation.

  2. In many real applications, sensitive information may only contained in some of the columns in data tables, so data owners just need to focus on those specific columns. i.e. attribute values in those specific columns are regarded as the corpus.

  3. LSH dimension means the number of features returned from LSH corresponding to a word in attribute values. Details will be introduced in the next section.

  4. This is an offline process conducted by data owners regularly. After updates of CFA, ACM and ACT are completed locally, both will be outsourced to the cloud.

  5. After several tuples are matched via ancillary columns, corresponding DET ciphertext will be returned for decryption. i.e. Ancillary columns are used for fuzzy searching, and all matched results are achieved by decrypting DET ciphertext.

  6. A word’s signature on each dimension is assured of an equal width by padding zero.

  7. We use popular n-gram methods described in Section 3.1.1 .

  8. Remark: In the clause ‘a__le%’, ‘a__le’ may refer to ‘apple’, ‘ankle’, etc, so ‘a__le’ can be treated as an unbroken word in the clause ‘a__le%’; On the contrary, ‘app%’ may refer to ‘applaud’, ‘applicant’, etc, so ‘app’ in the clause ‘app%’ is reckoned to be a fragment of words.

  9. This flag array provides a reference for our algorithm, and it will be revised in the process.

  10. We don’t consider the number of total underscores. e.g.mcu = 2 for ‘a__l_’.

  11. This is a limitation of Searchable Symmetric Encryption constructions [6, 12, 15, 20, 21], which is out of the scope of our scheme.

  12. About 5000 rows of data are processed in the c-LSH experiment while nearly 50000 rows of data are processed here.

  13. We construct these three structures separately in the experiment. In fact, the construction can be conducted simultaneously in real applications.

  14. So we evaluate the FSE (LSH) and FSE (BF) respectively with regard to c-LSH and c-BF.

  15. Following the standard of good accuracy stated above the accuracy metrics, if the result set from a scheme has false negative phenomenon, we won’t evaluate its # of false positive tuples anymore.

  16. Results from experiments in Section 5.2.4 are used here.

  17. e.g. If the original clause is ‘a__le%’, attribute values satisfying ‘apple%’, ‘ankle%’, etc, should all be retrieved.

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Acknowledgments

Supported by the National Key Research and Development Program of China (No. 2016YFB1000905), NSFC (Nos. 61772327, 61532021, U1501252, U1401256 and 61402180).

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Authors and Affiliations

  1. Faculty of Information, East China Normal University, Shanghai, 200062, China

    Jiaxun Hua, Yu Liu, He Chen & Cheqing Jin

  2. College of Computer Science & Technology, Shanghai University of Electric Power, Shanghai, 200090, China

    Xiuxia Tian

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  1. Jiaxun Hua
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Correspondence to Xiuxia Tian.

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Hua, J., Liu, Y., Chen, H. et al. An enhanced wildcard-based fuzzy searching scheme in encrypted databases. World Wide Web 23, 2185–2214 (2020). https://doi.org/10.1007/s11280-019-00774-x

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