Publication Type
Journal Article
Version
acceptedVersion
Publication Date
5-2024
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
Volume
17
Issue
3
First Page
789
Last Page
803
ISSN
1939-1374
Identifier
10.1109/TSC.2024.3376198
Publisher
Institute of Electrical and Electronics Engineers
Citation
CHEN, Zhenhua; HUANG, Luqi; YANG, Guomin; SUSILO, Willy; FU, Xingbing; and JIA, Xingxing.
Attribute-hiding fuzzy encryption for privacy-preserving data evaluation. (2024). IEEE Transactions on Services Computing. 17, (3), 789-803.
Available at: https://ink.library.smu.edu.sg/sis_research/8694
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TSC.2024.3376198