Visible to the public ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud

TitleClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud
Publication TypeConference Paper
Year of Publication2019
AuthorsZobaed, S.M., ahmad, sahan, Gottumukkala, Raju, Salehi, Mohsen Amini
Conference Name2019 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)
Date Publishedaug
KeywordsBig Data, big data security, big data security in the cloud, client-side encryption, cloud computing, cloud services, ClustCrypt, clustering, clustering methods, cryptography, Data analysis, data analytics, data encryption, data privacy, encrypted unstructured big data, Encryption, Indexes, Metrics, Organizations, pattern clustering, privacy, privacy-preserving data clustering, pubcrawl, resilience, Resiliency, Scalability, secure cloud-based semantic search system, topic-based clustering, unstructured
AbstractSecurity and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client machine before being stored in the cloud. Having encrypted data in the cloud, however, limits the ability of data clustering, which is a crucial part of many data analytics applications, such as search systems. To overcome the limitation, in this paper, we present an approach named ClustCrypt for efficient topic-based clustering of encrypted unstructured big data in the cloud. ClustCrypt dynamically estimates the optimal number of clusters based on the statistical characteristics of encrypted data. It also provides clustering approach for encrypted data. We deploy ClustCrypt within the context of a secure cloud-based semantic search system (S3BD). Experimental results obtained from evaluating ClustCrypt on three datasets demonstrate on average 60% improvement on clusters' coherency. ClustCrypt also decreases the search-time overhead by up to 78% and increases the accuracy of search results by up to 35%.
Citation Keyzobaed_clustcrypt_2019