TL;DR
SAED introduces an edge-based approach to enterprise search that enhances privacy by separating search intelligence from pattern matching, enabling semantic and personalized search without compromising data confidentiality.
Contribution
The paper presents SAED, a novel edge-based mechanism that provides semantic and personalized search capabilities while preserving privacy in cloud enterprise search systems.
Findings
Improves search result relevancy by 24% for plain-text datasets.
Enhances relevancy by 75% for encrypted datasets.
Can be integrated into existing systems without modifications.
Abstract
Cloud-based enterprise search services (e.g., AWS Kendra) have been entrancing big data owners by offering convenient and real-time search solutions to them. However, the problem is that individuals and organizations possessing confidential big data are hesitant to embrace such services due to valid data privacy concerns. In addition, to offer an intelligent search, these services access the user search history that further jeopardizes his/her privacy. To overcome the privacy problem, the main idea of this research is to separate the intelligence aspect of the search from its pattern matching aspect. According to this idea, the search intelligence is provided by an on-premises edge tier and the shared cloud tier only serves as an exhaustive pattern matching search utility. We propose Smartness At Edge (SAED mechanism that offers intelligence in the form of semantic and personalized…
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