Enhancing Keyword Search Privacy In Encrypted Cloud Data Through Optimization

Abstract

As cloud computing gains prominence, organizations find themselves compelled to shift Moving their intricate data management systems from on-premise locations to private cloud providers, seeking enhanced flexibility and cost-effectiveness. Nonetheless, to safeguard sensitive data, it is crucial to encrypt it before externalizing. In order to address the demand for efficient data retrieval, these search services must support multi-keyword queries and offer similarity ratings for results, considering the substantial volume of data and documents stored in cloud storage. Efforts in searchable encryption often overlook the distinction between search results, typically concentrating on single-phrase or Boolean

keyword searches. This research represents a notable progress as it introduces and tackles

The complex problem of privacy-preserving multi-keyword ranking, ontology keyword

Mapping, and search over encrypted cloud data (EARM). Additionally, we establish a stringent set of privacy guidelines that must be followed for The deployment of a system for utilizing cloud data securely. Our preferred approach for measuring similarity between search queries and data documents involves the effective principle of "Enhanced Association Rule Mining coordinate matching," emphasizing the capture of as many matches as possible in the context of multiple-keyword semantics.

assessment, we employ the concept of "inner product similarity." Initially, we introduce a fundamental EARM technique using secure inner product computing, which is subsequently enhanced to satisfy various privacy criteria across two threat model levels