Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2024

Abstract

Privacy-preserving data evaluation is one of the prominent research topics in the big data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this paper, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap distance, which can work in an integer domain while achieving attribute-hiding. In particular, we develop a novel approach to enable an accurate overlap distance to be fast calculated. This technique makes the number of pairing operations during decryption stage negative correlation with the size of the threshold, which is pretty practical for some applications especially with a large threshold. Additionally, we provide a formal security analysis of the proposed scheme, followed by a comprehensive experimental. Also we show that our scheme can be well applied to some scenarios, such as fuzzy keyword searchable encryption and attribute-hiding closest substring encryption.

Keywords

attribute-hiding, data evaluation, Encryption, Fuzzy encryption, Hamming distances, Inspection, Medical diagnostic imaging, overlap distance, predicate encryption, Privacy, Security, Vectors

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Services Computing

First Page

1

Last Page

15

ISSN

1939-1374

Identifier

10.1109/TSC.2024.3376198

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TSC.2024.3376198

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