计算机科学
方案(数学)
信息隐私
随机预言
甲骨文公司
私人信息检索
查询优化
Web查询分类
隐私软件
数据挖掘
Web搜索查询
情报检索
加密
计算机安全
公钥密码术
搜索引擎
软件工程
数学分析
数学
作者
Yandong Zheng,Hui Zhu,Rongxing Lu,Yunguo Guan,Songnian Zhang,Fengwei Wang,Jun Shao,Hui Li
标识
DOI:10.1109/tifs.2023.3293416
摘要
The aggregated reverse kNN (ARkNN) query aims to identify one query record with the maximum influence set and has become a powerful tool to support optimal decision-making in crowdsensing. Considering data privacy and query privacy, ARkNN queries should be performed in a private manner. Unfortunately, existing schemes cannot support privacy-preserving ARkNN queries over crowd-sensed data. To address this issue, we propose two efficient and privacy-preserving ARkNN query schemes with different security levels, named the BARQ scheme and the EARQ scheme, where the former can only protect data privacy while the latter can protect both data privacy and query privacy. Specifically, we first formalize the models of privacy-preserving ARkNN queries and propose our BARQ scheme based on a random response (RR) frequency oracle. Then, we design a privacy-preserving hardware-assisted reverse kNN query determination (PRkD) scheme for privately determining whether a query record is among the RkNN of a data record. After that, we present our EARQ scheme by leveraging the PRkD scheme to protect query privacy and integrating the RR frequency oracle to protect data privacy. In addition, our rigorous security analysis demonstrates that the BARQ scheme can well protect data privacy, and the EARQ scheme can protect both data privacy and query privacy. Extensive experimental results illustrate that they have high accuracy in query results and are efficient in computational costs and communication overheads.
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