Edge Computing for User-Centric Secure Search on Cloud-Based Encrypted Big Data
Title | Edge Computing for User-Centric Secure Search on Cloud-Based Encrypted Big Data |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | ahmad, sahan, Zobaed, SM, Gottumukkala, Raju, Salehi, Mohsen Amini |
Conference Name | 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) |
Keywords | Big Data, big data encryption, big data security, big dataset clustering, cloud computing, cloud provider, cloud service providers, cloud-based solutions, cryptography, data control, data privacy, data structures, edge computing, Encrypted Clustering, Encryption, Markov chain, Metrics, multisource Big Data search space, pattern clustering, Privacy-Preserving Big Data, pruning system, pubcrawl, Resiliency, Scalability, semantic search, sensitive data, user privacy in the cloud, User-based Sampling, user-centric cloud services, user-centric search ability, user-centric search system, user-centric secure search, user-side encryption |
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 multiorganizational (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 realtime 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. |
DOI | 10.1109/HPCC/SmartCity/DSS.2019.00100 |
Citation Key | ahmad_edge_2019 |
- sensitive data
- multisource Big Data search space
- pattern clustering
- Privacy-Preserving Big Data
- pruning system
- pubcrawl
- Resiliency
- Scalability
- semantic search
- Metrics
- user privacy in the cloud
- User-based Sampling
- user-centric cloud services
- user-centric search ability
- user-centric search system
- user-centric secure search
- user-side encryption
- big data security
- markov chain
- encryption
- Encrypted Clustering
- edge computing
- data structures
- data privacy
- data control
- Cryptography
- cloud-based solutions
- cloud service providers
- cloud provider
- Cloud Computing
- big dataset clustering
- big data encryption
- Big Data