SECURE DATA MANAGEMENT SYSTEM WITH ENHANCED AUTHENTICATIONAND ACCESS CONTROL

Abstract

The emergence of cloud computing, data owners are being driven to move their intricate data management systems from on-site locations to private cloud providers in order to take advantage of increased flexibility and cost savings. However, sensitive data must be encrypted before being outsourced in order to safeguard data privacy. This renders outdated the conventional method of using data, which relies on plaintext keyword searches. It is therefore crucial to activate an encrypted cloud data search service. In order to satisfy the effective data retrieval demand, search services must support multi-keyword queries and offer result similarity rating, given the volume of data users and documents stored in cloud storage. Similar efforts on searchable encryption seldom distinguish between search results and concentrate on single keyword or Boolean keyword search. In this paper, we formulate and solve the difficult problem of privacy-preserving multi-keyword ranked ontology keyword mapping and search over encrypted cloud data (EARM) for the first time. We also define a stringent set of privacy requirements that must be met in order to realize such a secure cloud data utilization system. The effective principle of "Enhanced Association Rule Mining coordinate matching," or capturing as many matches as possible, is our choice among multiple-keyword semantics for measuring similarity between search queries and data documents. We then use "inner product similarity" to quantitatively formalize this principle for similarity measurement.