Effective Cloud Search Based on Multi Keyword Ranked Over Encrypted Cloud Data

In recent years, consumer-centric cloud computing paradigm has emerged as the development of smart electronic devices combined with the emerging cloud computing technologies. A variety of cloud services are delivered to the consumers with the premise that an effective and efficient cloud search service is achieved. For consumers, they want to find the most relevant products or data, which is highly desirable in the “pay-as-you use” cloud computing paradigm. As sensitive data (such as photo albums, emails, personal health records, financial records, etc.) are encrypted before outsourcing to cloud, traditional keyword search techniques are useless. Meanwhile, existing search approaches over encrypted cloud data support only exact or fuzzy keyword search, but not semantics-based multi-keyword ranked search. Therefore, how to enable an effective searchable system with support of ranked search remains a very challenging problem. This paper proposes an effective approach to solve the problem of multi-keyword ranked search over encrypted cloud data supporting synonym queries. The main contribution of this paper is summarized in two aspects: multi-keyword ranked search to achieve more accurate search results and synonym-based search to support synonym queries. The ranked search enables cloud customers to find the most relevant information quickly. Ranked search can also reduce network traffic as the cloud server sends back only the most relevant data. Multikeyword search is also very important to improve search result accuracy as single keyword search often return coarse search results. To meet the challenge of effective search system, this paper proposes a practically efficient and flexible searchable scheme which supports both multi-keyword ranked search and synonym-based search. To address multi-keyword search and result ranking, Vector Space Model (VSM) (says database,) is used to build document index, that is to say, each document is expressed as a vector where each dimension value is the Term Frequency (TF) weight of its corresponding keyword. The contributions of this paper are summarized as follows: For the first time, a semantics-based multi-keyword ranked search technology over encrypted cloud data which supports synonym queries is proposed. The search results can be achieved when authorized cloud customers input the synonyms of the predefined keywords, not the exact or fuzzy matching keywords, due to the possible synonym substitution and/or her lack of exact knowledge about the data. Extensive experiments on the real-world dataset further show the effectiveness and efficiency of proposed solution.