Privacy-Preserving Attribute-Based Keyword Search with Traceability and Revocation for Cloud-Assisted IoT

被引:4
|
作者
Zhang, Kai [1 ]
Li, Yanping [1 ]
Lu, Laifeng [1 ]
机构
[1] Shaanxi Normal Univ, Sch Math & Stat, Xian 710119, Peoples R China
基金
中国国家自然科学基金;
关键词
IDENTITY-BASED ENCRYPTION; GRAINED ACCESS-CONTROL; DATA SHARING FRAMEWORK; OF-THE-ART; INDUSTRIAL INTERNET; POLICY; THINGS;
D O I
10.1155/2021/9929663
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the rapid development of cloud computing and Internet of Things (IoT) technology, it is becoming increasingly popular for source-limited devices to outsource the massive IoT data to the cloud. How to protect data security and user privacy is an important challenge in the cloud-assisted IoT environment. Attribute-based keyword search (ABKS) has been regarded as a promising solution to ensure data confidentiality and fine-grained search control for cloud-assisted IoT. However, due to the fact that multiple users may have the same retrieval permission in ABKS, malicious users may sell their private keys on the Internet without fear of being caught. In addition, most of existing ABKS schemes do not protect the access policy which may contain privacy information. Towards this end, we present a privacy-preserving ABKS that simultaneously supports policy hiding, malicious user traceability, and revocation. Formal security analysis shows that our scheme can not only guarantee the confidentiality of keywords and access policies but also realize the traceability of malicious users. Furthermore, we provide another more efficient construction for public tracing.
引用
收藏
页数:13
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