Lightweight attribute-based keyword search with policy protection for cloud-assisted IoT
With the deep integration of the Internet of Things (IoT) and cloud computing, cloud-oriented IoT is embraced as an important paradigm for efficiency and productivity. On the other hand, it is also becoming an increasingly attractive target for cybercriminals, who attempt to breach data security and...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2019
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5071 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-6074 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-60742020-03-12T06:54:03Z Lightweight attribute-based keyword search with policy protection for cloud-assisted IoT SUN, Jianfei XIONG, Hu DENG, Robert H. ZHANG, Yinghui LIU, Ximeng CAO, Mingsheng With the deep integration of the Internet of Things (IoT) and cloud computing, cloud-oriented IoT is embraced as an important paradigm for efficiency and productivity. On the other hand, it is also becoming an increasingly attractive target for cybercriminals, who attempt to breach data security and privacy. As a potential and promising solution to secure data, ciphertext-policy attribute-based keyword search (CP-ABKS) can provide both fine-grained keyword search and access control over the encrypted data. However, prior CP-ABKS schemes either fail to support lightweight computation or lack of policy protection. In this paper, with offline computation and inner product encryption, we propose a lightweight CP-ABKS scheme with policy protection, such that the encrypted data can be efficiently retrieved and accessed by data users in a fine-grained manner without leaking any sensitive information. We prove the correctness of the proposed scheme and its security in the standard model under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. We also implement our proposed scheme to demonstrate its efficiency. 2019-11-20T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/5071 info:doi/10.1109/DSC47296.2019.8937708 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University attribute-based keyword search Internet of Things policy protection 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-based keyword search Internet of Things policy protection Information Security |
spellingShingle |
attribute-based keyword search Internet of Things policy protection Information Security SUN, Jianfei XIONG, Hu DENG, Robert H. ZHANG, Yinghui LIU, Ximeng CAO, Mingsheng Lightweight attribute-based keyword search with policy protection for cloud-assisted IoT |
description |
With the deep integration of the Internet of Things (IoT) and cloud computing, cloud-oriented IoT is embraced as an important paradigm for efficiency and productivity. On the other hand, it is also becoming an increasingly attractive target for cybercriminals, who attempt to breach data security and privacy. As a potential and promising solution to secure data, ciphertext-policy attribute-based keyword search (CP-ABKS) can provide both fine-grained keyword search and access control over the encrypted data. However, prior CP-ABKS schemes either fail to support lightweight computation or lack of policy protection. In this paper, with offline computation and inner product encryption, we propose a lightweight CP-ABKS scheme with policy protection, such that the encrypted data can be efficiently retrieved and accessed by data users in a fine-grained manner without leaking any sensitive information. We prove the correctness of the proposed scheme and its security in the standard model under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. We also implement our proposed scheme to demonstrate its efficiency. |
format |
text |
author |
SUN, Jianfei XIONG, Hu DENG, Robert H. ZHANG, Yinghui LIU, Ximeng CAO, Mingsheng |
author_facet |
SUN, Jianfei XIONG, Hu DENG, Robert H. ZHANG, Yinghui LIU, Ximeng CAO, Mingsheng |
author_sort |
SUN, Jianfei |
title |
Lightweight attribute-based keyword search with policy protection for cloud-assisted IoT |
title_short |
Lightweight attribute-based keyword search with policy protection for cloud-assisted IoT |
title_full |
Lightweight attribute-based keyword search with policy protection for cloud-assisted IoT |
title_fullStr |
Lightweight attribute-based keyword search with policy protection for cloud-assisted IoT |
title_full_unstemmed |
Lightweight attribute-based keyword search with policy protection for cloud-assisted IoT |
title_sort |
lightweight attribute-based keyword search with policy protection for cloud-assisted iot |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2019 |
url |
https://ink.library.smu.edu.sg/sis_research/5071 |
_version_ |
1770575205664555008 |