  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.
