Journal
2022 IEEE 42ND INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2022)
Volume -, Issue -, Pages 392-402Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICDCS54860.2022.00045
Keywords
spatio-textual data; outsourcing data; cloud server; privacy-preserving; spatial keyword query
Categories
Funding
- National Natural Science Foundation of China [61872283, 61802243, U21A20464, 62125205, 62072361]
- Key Research and Development Program of Shaanxi [2022GY-019]
- Fundamental Research Funds for the Central Universities [JB211505]
- State Key Laboratory of Mathematical Engineering and Advanced Computing [LNCT2020-A06]
- Natural Science Basic Research Program of Shaanxi [2021JC-22]
- China 111 Project [B16037]
- Cloud Technology Endowed Professorship
- Henan Key Laboratory of Network Cryptography Technology [LNCT2020-A06]
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This paper presents an enhanced spatial keyword query scheme that utilizes random numbers and a random permutation to enhance encryption security. Furthermore, a privacy-preserving spatial keyword query scheme is proposed based on the enhanced encryption scheme and the Geohash algorithm. Additionally, a lightweight spatial keyword query scheme is designed by using a unified index for query, reducing storage and computational costs.
With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatio-textual data is outsourced to the cloud server to reduce the local high storage and computing burdens, but at the same time causes security issues such as data privacy leakage. Thus, extensive privacy-preserving spatial keyword query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) for encryption, but ASPE has proven to be insecure. And the existing spatial range query schemes require users to provide more information about the query range and generate a large amount of ciphertext, which causes high storage and computational burdens. To solve these issues, in this paper we introduce some random numbers and a random permutation to enhance the security of ASPE scheme, and then propose a novel privacy-preserving Spatial Keyword Query (SKQ) scheme based on the enhanced ASPE and Geohash algorithm. In addition, we design a more Lightweight Spatial Keyword Query (LSKQ) scheme by using a unified index for spatial range and multiple keywords, which not only greatly decreases SKQ's storage and computational costs but also requires users to provide little information about query region. Finally, formal security analysis proves that our schemes have Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our enhanced scheme is efficient and practical.
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