Privacy-Preserving Ranked Spatial Keyword Query in Mobile Cloud-Assisted Fog Computing
Qiuyun Tong, Yinbin Miao, Hongwei Li, Ximeng Liu, Robert H. Deng
Abstract
With the increasing popularity of GPS-equipped mobile devices in cloud-assisted fog computing scenarios, massive spatio-textual data is generated and outsourced to cloud servers for storage and analysis. Existing privacy-preserving range query or ranked keyword search schemes does not support a unified index, and are just applicable for the symmetric environment where all users sharing the same secret key. To solve this issue, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy-preserving <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> anked <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> patial keyword <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</u> uery in mobile cloud-assisted <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> og computing (PRSQ-F). Specifically, we design a novel comparable product encoding strategy that combines both spatial and textual conditions tightly to retrieve the objects in query range and with the highest textual similarity. Then, we use a new conversion protocol and attribute-based encryption to support privacy-preserving retrieval and malicious user traceability in the asymmetric environment where different query users have different keys. Furthermore, we construct an R-tree-based index to achieve faster-than-linear retrieval. Our formal security analysis shows that data security can be guaranteed. Our empirical experiments using a real-world dataset demonstrate the efficiency and feasibility of PRSQ-F.