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.

中文翻译：

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SAED：基于边缘的情报可用于在Clou上保护隐私的企业搜索

基于云的企业搜索服务（例如，AWS Kendra）通过为大数据所有者提供便捷的实时搜索解决方案而吸引了他们。但是，问题在于，由于有效的数据隐私问题，拥有机密大数据的个人和组织不愿接受此类服务。另外，为了提供智能搜索，这些服务将访问用户搜索历史记录，这进一步危害了他/她的隐私。为了克服隐私问题，该研究的主要思想是将搜索的智能方面与模式匹配方面分开。根据此想法，搜索智能由本地边缘层提供，并且共享云层仅用作详尽的模式匹配搜索实用程序。