计算机科学
潜在Dirichlet分配
杠杆(统计)
符号
加密
语义搜索
理论计算机科学
安全性分析
无线自组网
信息泄露
情报检索
数据挖掘
计算机安全
人工智能
数学
主题模型
搜索引擎
算术
无线
电信
作者
Jiayi Li,Jianfeng Ma,Yinbin Miao,Fan Yang,Ximeng Liu,Kim‐Kwang Raymond Choo
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2021-09-01
卷期号:70 (9): 8912-8925
被引量:6
标识
DOI:10.1109/tvt.2021.3098177
摘要
Vehicular Ad hoc Networks (VANETs) play an increasingly important role in a number of applications, particularly those associated with location-based services (e.g., spatial keyword searches – SKS). However, there is a need to strike a balance between privacy guarantee and search efficiency, and existing SKS solutions cannot be directly implemented in VANETs. In addition, existing schemes may also lack support for semantic-awareness in the dynamic setting. To address these limitations, we propose a Secure Semantic-aware Spatial Keyword Search scheme that supports Dynamic update (3SKSD). Specifically, we leverage the Latent Dirichlet Allocation (LDA) topic model and secure $k$ Nearest Neighbor ( $k$ NN) method to help us achieve both efficiency and security. We also construct an encrypted R-tree structure to facilitate SKS and dynamic update. Moreover, we propose an advanced scheme with forward security on the basis of 3SKSD, with the aim of minimizing privacy leakage due to dynamic updates. Our formal security analysis verifies the validity and security of 3SKSD, and findings from the experimental evaluation demonstrate its high search accuracy and efficiency.
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