Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2023
Abstract
Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked Keyword Search (ML-RKS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RKS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, we propose an Enhanced ML-RKS (called ML-RKS+) scheme by introducing a permutation matrix. ML-RKS+ prevents cloud servers from making search queries over newly added files via previous tokens, thereby achieving forward security. The security analysis proves that our schemes protect the privacy of indexes, query tokens and keywords. Empirical experiments using the real-world dataset demonstrate that our schemes are efficient and feasible in practical applications.
Keywords
Binary trees, Complexity theory, Cryptography, Indexes, Keyword search, Security, Servers
Discipline
Information Security
Research Areas
Cybersecurity
Publication
IEEE Transactions on Services Computing
Volume
16
Issue
1
First Page
525
Last Page
536
ISSN
1939-1374
Identifier
10.1109/TSC.2021.3140098
Publisher
Institute of Electrical and Electronics Engineers
Citation
MIAO, Yinbin; ZHENG, Wei; JIA, Xiaohua; LIU, Ximeng; CHOO, Kim-Kwang Raymond; and DENG, Robert H..
Ranked keyword search over encrypted cloud data through machine learning method. (2023). IEEE Transactions on Services Computing. 16, (1), 525-536.
Available at: https://ink.library.smu.edu.sg/sis_research/6934
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.2021.3140098