Attribute-hiding fuzzy encryption for privacy-preserving data evaluation
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...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2024
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/8694 https://ink.library.smu.edu.sg/context/sis_research/article/9697/viewcontent/Attribute_hidingFuzzy_2024_av.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-9697 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-96972024-07-05T05:44:03Z Attribute-hiding fuzzy encryption for privacy-preserving data evaluation CHEN, Zhenhua HUANG, Luqi YANG, Guomin SUSILO, Willy FU, Xingbing JIA, Xingxing 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. 2024-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8694 info:doi/10.1109/TSC.2024.3376198 https://ink.library.smu.edu.sg/context/sis_research/article/9697/viewcontent/Attribute_hidingFuzzy_2024_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University attribute-hiding data evaluation Encryption Fuzzy encryption Hamming distances Inspection Medical diagnostic imaging overlap distance predicate encryption Privacy Security Vectors Information Security |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
attribute-hiding data evaluation Encryption Fuzzy encryption Hamming distances Inspection Medical diagnostic imaging overlap distance predicate encryption Privacy Security Vectors Information Security |
spellingShingle |
attribute-hiding data evaluation Encryption Fuzzy encryption Hamming distances Inspection Medical diagnostic imaging overlap distance predicate encryption Privacy Security Vectors Information Security CHEN, Zhenhua HUANG, Luqi YANG, Guomin SUSILO, Willy FU, Xingbing JIA, Xingxing Attribute-hiding fuzzy encryption for privacy-preserving data evaluation |
description |
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. |
format |
text |
author |
CHEN, Zhenhua HUANG, Luqi YANG, Guomin SUSILO, Willy FU, Xingbing JIA, Xingxing |
author_facet |
CHEN, Zhenhua HUANG, Luqi YANG, Guomin SUSILO, Willy FU, Xingbing JIA, Xingxing |
author_sort |
CHEN, Zhenhua |
title |
Attribute-hiding fuzzy encryption for privacy-preserving data evaluation |
title_short |
Attribute-hiding fuzzy encryption for privacy-preserving data evaluation |
title_full |
Attribute-hiding fuzzy encryption for privacy-preserving data evaluation |
title_fullStr |
Attribute-hiding fuzzy encryption for privacy-preserving data evaluation |
title_full_unstemmed |
Attribute-hiding fuzzy encryption for privacy-preserving data evaluation |
title_sort |
attribute-hiding fuzzy encryption for privacy-preserving data evaluation |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2024 |
url |
https://ink.library.smu.edu.sg/sis_research/8694 https://ink.library.smu.edu.sg/context/sis_research/article/9697/viewcontent/Attribute_hidingFuzzy_2024_av.pdf |
_version_ |
1814047650231615488 |