Paper Contents
Abstract
Data owners are being encouraged to move their complex data management systems from local locations to commercial public clouds for greater flexibility and cost savings with the advent of cloud computing. However, sensitive data must be encrypted before being outsourced in order to protect privacy, which renders plaintext keyword search as a method of data utilization obsolete. Consequently, it is essential to implement an encrypted cloud data search service. For effective data retrieval, it is essential for the search service to support multi-keyword queries and result similarity ranking due to the large number of cloud data users and documents. Studies on searchable encryption tend to concentrate on Boolean keyword searches or searches with just one term, with few attempts made to differentiate the search results. In this project, for the first time, we define and solve the difficult problem of privacy-preserving multi-keyword ranked (EARM). Additionally, we establish stringent privacy requirements that must be met for such a secure cloud data utilization system to become a reality. We choose the effective principle of "Boolean keyword coordinate matching," or as many matches as possible, to capture the similarity between the search query and data documents. Additionally, we employ "inner product similarity" to quantitatively formalize this principle for measuring similarity. First, we present a straightforward EARM system that is based on safe inner product computing. We then significantly expand this system to meet distinct privacy requirements in two threat scenarios. The privacy and efficiency guarantee of the proposed strategies are thoroughly examined, and tests on real-world datasets show that the proposed strategies do not significantly increase computation or communication costs.
Copyright
Copyright © 2023 Nandagopal S, BharathKumar S, Lakshmanakumar V, NaveenKumar M. This is an open access article distributed under the Creative Commons Attribution License.
