Journal
IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS
Volume -, Issue -, Pages 2253-2262Publisher
IEEE
DOI: 10.1109/infocom41043.2020.9155505
Keywords
privacy-preserving; Boolean range queries; encrypted spatial data
Categories
Funding
- Key Program of NSFC [U1405255]
- Shaanxi Science & Technology Coordination & Innovation Project [2016TZC-G-6-3]
- Fundamental Research Funds for the Central Universities [SA-ZD161504, JB191506]
- National Natural Science Foundation of China [61702404, U1804263, 61702105]
- National Natural Science Foundation of Shaanxi Province [2019JQ-005]
- Doctoral Students' Short Term Study Abroad Scholarship, Xidian University
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With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least 70x faster than existing techniques in the literature.
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