兰州大学机构库
Attribute-hiding fuzzy encryption for privacy-preserving data evaluation
Z. Chen; L. Huang; G. Yang; W. Susilo; X. Fu; X. Jia
2024-03-12
Source PublicationIEEE Transactions on Services Computing   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN1939-1374
VolumePPIssue:99Pages:1-15
AbstractPrivacy-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.
KeywordFuzzy encryption predicate encryption attribute-hiding data evaluation overlap distance
PublisherIEEE
DOI10.1109/TSC.2024.3376198
Indexed ByIEEE
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