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Cloud service providers offer a low-cost and convenient solution to hostunstructured data. However, cloud services act as third-party solutions and donot provide control of the data to users. This has raised security and privacyconcerns for many organizations (users) with sensitive data to utilizecloud-based solutions. User-side encryption can potentially address theseconcerns by establishing user-centric cloud services and granting data controlto the user. Nonetheless, user-side encryption limits the ability to process(e.g., search) encrypted data on the cloud. Accordingly, in this research, weprovide a framework that enables processing (in particular, searching) ofencrypted multi-organizational (i.e., multi-source) big data without revealingthe data to cloud provider. Our framework leverages locality feature of edgecomputing to offer a user-centric search ability in a real-time manner. Inparticular, the edge system intelligently predicts the user's search patternand prunes the multi-source big data search space to reduce the search time.The pruning system is based on efficient sampling from the clustered bigdataset on the cloud. For each cluster, the pruning system dynamically samplesappropriate number of terms based on the user's search tendency, so that thecluster is optimally represented. We developed a prototype of a user-centricsearch system and evaluated it against multiple datasets. Experimental resultsdemonstrate 27% improvement in the pruning quality and search accuracy.

Edge Computing for User-Centric Secure Search on Cloud-Based Encrypted Big Data