2019 IEEE 35th International Conference on Data Engineering (ICDE) 2019
DOI: 10.1109/icde.2019.00097
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When Geo-Text Meets Security: Privacy-Preserving Boolean Spatial Keyword Queries

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Cited by 50 publications
(6 citation statements)
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“…Boolean range spatial keyword (BRSK) query [41,45,49,68,83,102,135,163,177]. A BRSK query q = (BE, ρ, r) takes three parameters: BE is a Boolean keyword expression that is composed of a set of keywords connected by AND or OR operators, ρ is a query location, and r is a query region radius.…”
Section: Standard Queriesmentioning
confidence: 99%
See 1 more Smart Citation
“…Boolean range spatial keyword (BRSK) query [41,45,49,68,83,102,135,163,177]. A BRSK query q = (BE, ρ, r) takes three parameters: BE is a Boolean keyword expression that is composed of a set of keywords connected by AND or OR operators, ρ is a query location, and r is a query region radius.…”
Section: Standard Queriesmentioning
confidence: 99%
“…Most existing studies (e.g., [41,45,49,68,102,135,177]) consider a simplified BRSK query that considers a Boolean keyword expression composed of AND operators only. This query is given by q = (ψ, ρ, r), and the result q (D) of q is the subset of D satisfying ∀o ∈ q (D)(dist(o, q) ≤ q.r ∧ q.ψ ⊆ o.ψ).…”
Section: Standard Queriesmentioning
confidence: 99%
“…Performance. We compare our SSKQ with existing privacypreserving BRQ solutions ELCBFR+ [16] and PBRQ-Q [18]. Fig.…”
Section: Performance Evaluationmentioning
confidence: 99%
“…Many schemes [9][10][11][12][13][14][15] have been proposed to support secure spatial queries, but these schemes cannot be directly used to support secure spatial keyword queries. To adress this issue, Cui et al [16] proposed the first privacy-preserving boolean spatial keyword queries scheme to support BRQ on encrypted data. However, their solution was constructed based on the Asymmetric Scalar Product-preserving Encryption (ASPE) [11], which has been proven to be too weak to resist even Ciphertext-Only Attack (COA) [17].…”
Section: Introductionmentioning
confidence: 99%
“…Input perturbation also allows to share the sensitive data more securely to other parties. When an outsourcing is done for classification of data on the cloud, the cloud server is assumed to be semi-honest in general, and it is likely to be untrusted and malicious (Cui et al, 2019;Cui, Yang, Wang, Li, & Wang, 2020). Recently, Cui et al (2020) have proposed an efficient, secure, and verifiable k-NN query on the cloud platform by employing input perturbation as the first step, and then applying homomorphic encryption to the data before sending it to the cloud server.…”
Section: Introductionmentioning
confidence: 99%