SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud
SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud
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DOI：
10.1109/ccgrid51090.2021.00046
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Sakib Zobaed;M. Salehi;R. Buyya
中科院分区：
文献类型：
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作者：
Sakib Zobaed;M. Salehi;R. Buyya
基于云的企业搜索服务（例如，AWS Kendra）一直通过向大数据所有者提供方便和实时的搜索解决方案来吸引他们。然而，问题是，拥有机密大数据的个人和组织由于有效的数据隐私问题而对接受此类服务犹豫不决。此外，为了提供智能搜索，这些服务会访问用户的搜索历史记录，这进一步损害了他/她的隐私。为了克服隐私问题，本研究的主要思想是将搜索的智能方面与其模式匹配方面分开。根据这个想法，搜索智能由本地边缘层提供，共享云层仅用作穷举模式匹配搜索实用程序。我们提出了边缘智能（SAED）机制，该机制在边缘层以语义和个性化搜索的形式提供智能，同时在云层保持搜索的隐私。在边缘层，SAED使用基于知识的词汇数据库来扩展查询并覆盖其语义。SAED通过可以学习用户兴趣的RNN模型来个性化搜索。一个词嵌入模型用于检索文档的基础上，他们的语义相关性的搜索查询。SAED是通用的，可以插入现有的企业搜索系统，使他们能够提供智能和隐私保护的搜索，而无需对它们进行任何更改。在真实的环境下对两个企业搜索系统的评估结果表明，SAED可以提高检索结果的相关性，对于纯文本数据集平均提高24%，对于加密的通用数据集平均提高75%。
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’s 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’s 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.