Achieve Efficient Position-Heap-based Privacy-Preserving Substring-of-Keyword Query over Cloud

TL;DR

This paper presents a novel position-heap-based scheme enabling efficient, privacy-preserving substring-of-keyword queries over encrypted data in cloud environments, addressing limitations of existing exact keyword search methods.

Contribution

It introduces a new tree-based index using position heaps for substring matching, supporting privacy-preserving substring queries in cloud data search.

Findings

The scheme achieves efficient substring search over encrypted data.

Security analysis confirms the scheme's privacy-preserving properties.

Experimental results demonstrate improved performance over existing methods.

Abstract

The cloud computing technique, which was initially used to mitigate the explosive growth of data, has been required to take both data privacy and users' query functionality into consideration. Symmetric searchable encryption (SSE) is a popular solution to supporting efficient keyword queries over encrypted data in the cloud. However, most of the existing SSE schemes focus on the exact keyword query and cannot work well when the user only remembers the substring of a keyword, i.e., substring-of-keyword query. This paper aims to investigate this issue by proposing an efficient and privacy-preserving substring-of-keyword query scheme over cloud. First, we employ the position heap technique to design a novel tree-based index to match substrings with corresponding keywords. Based on the tree-based index, we introduce our substring-of-keyword query scheme, which contains two consecutive phases. The first phase queries the keywords that match a given substring, and the second phase queries the files that match a keyword in which people are really interested. In addition, detailed security analysis and experimental results demonstrate the security and efficiency of our proposed scheme.