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Top1. Introduction
Over the past decades, the internet has highly affected and changed people’s lives (Mahajan et al., 2018; Muliawat et al., 2019 (especially in the healthcare sector. where people moved their data to cloud computing since data is getting bigger and needs to be accessible from many devices. But, this data may be exposed to malicious attacks that may threaten its privacy and availability such as Denial of Service (DOS) attack (Hosny et al., 2020), phishing attack, and Man-in-the-middle (MITM) attack,…etc. Hence, the e-government should have a major role in the implementation and practice of e-health, represented in maintaining the privacy of data) Lyamu, 2020). One of the existing solutions is to cipher the user's sensitive data before being transferred to the cloud which in turn made the query process and its privacy a great challenge. Therefore, it is necessary to establish a secure querying scheme to retrieve the encrypted data while maintaining the query privacy.
With the use of searchable encryption techniques, users become able to query the ciphered data without having to download and decipher all data. They allow users to execute keyword queries over encrypted data directly based on the encrypted indexes and queries while unseeing the index and query. These techniques are classified into several categories depending on several factors such as the key used for encryption, data storage, number of keywords used in the search operation, and the query type,…etc. (Al-Utaibi& El-Alfy, 2017). As the number of data users and the number of documents is increased, it is more convenient to use Searchable Symmetric Encryption (SSE) due to its functionality and efficiency (Chen et al., 2017).
Many researchers adopt various types of search function (Chen et al., 2017; Meharwade &Patil, 2016; Gurjar & Pasupuleti, 2016; Gowda & Sumathi, 2017; Mathew & Babu, 2015) such as single keyword search, multi-keyword ranked search, multi-keyword Boolean search, similarity search, and multi-keyword verifiable search. Multi-keyword search becomes trendier in research work as each query keyword helps to get closer to the result of a search, unlike a single keyword search which often results in an unsatisfactory search result (Al-Utaibi& El-Alfy, 2017). On the other side, to retrieve the most relevant documents quickly, the cloud server has to perform ranking based on the similarity between the documents and search query. Although there exist many schemes (Raghavendra et al., 2018; Ding et al., 2017) that support multi-keyword ranked search, they still need further improvements in their performance such as preserving the query privacy and reduce the time and memory consumption used to query the data.
This paper proposes an efficient fuzzy keyword querying scheme to query the encrypted healthcare data while maintaining the query privacy. Here, the encrypted healthcare data means the Patient Health Record (PHR) which is an electronic format of a collection of critical information that patients keep about their health.
In the proposed scheme, the Rapid Automatic Keyword Extraction (RAKE) algorithm is combined with the wildcard- based fuzzy set construction algorithm and the sparse Vector Space Model (VSM) to build the searchable index. So, it reduces the time and memory consumption required for the querying process. As well as, the cloud server is prevented from distinguishing between queries having the same keywords by adding a random value to each value in the query's vector. So the scheme maintains the query privacy.