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

## TL;DR

This paper presents an edge computing framework that enables secure, real-time search over encrypted multi-source big data, enhancing privacy and efficiency by predicting user search patterns and intelligently pruning data search space.

## Contribution

It introduces a novel user-centric search framework leveraging edge computing to process encrypted big data without revealing sensitive information, improving search accuracy and speed.

## Key findings

- Achieved 27% improvement in pruning quality
- Enhanced search accuracy over multiple datasets
- Demonstrated real-time, user-centric search capability

## Abstract

Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this research, we provide a framework that enables processing (in particular, searching) of encrypted multi-organizational (i.e., multi-source) big data without revealing the data to cloud provider. Our framework leverages locality feature of edge computing to offer a user-centric search ability in a real-time manner. In particular, the edge system intelligently predicts the user's search pattern and prunes the multi-source big data search space to reduce the search time. The pruning system is based on efficient sampling from the clustered big dataset on the cloud. For each cluster, the pruning system dynamically samples appropriate number of terms based on the user's search tendency, so that the cluster is optimally represented. We developed a prototype of a user-centric search system and evaluated it against multiple datasets. Experimental results demonstrate 27% improvement in the pruning quality and search accuracy.