SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud

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 search at the edge tier while maintaining privacy of the search on the cloud tier. At the edge tier, SAED uses a knowledge-based lexical database to expand the query and cover its semantics. SAED personalizes the search via an RNN model that can learn the user interest. A word embedding model is used to retrieve documents based on their semantic relevance to the search query. SAED is generic and can be plugged into existing enterprise search systems and enable them to offer intelligent and privacy-preserving search without enforcing any change on them. Evaluation results on two enterprise search systems under real settings and verified by human users demonstrate that SAED can improve the relevancy of the retrieved results by on average 24% for plain-text and 75% for encrypted generic datasets.