ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud
ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud
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DOI：
10.1109/hpcc/smartcity/dss.2019.00093
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
S. Zobaed;Sahan Ahmad;Raju N. Gottumukkala;M. Salehi
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文献类型：
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作者：
S. Zobaed;Sahan Ahmad;Raju N. Gottumukkala;M. Salehi
存储在云中的大数据的安全性和机密性是许多组织采用云服务的重要考虑因素。解决这些问题的一种常见方法是客户端加密，其中数据在存储到云中之前在客户端机器上加密。然而，在云中加密数据限制了数据聚类的能力，这是许多数据分析应用程序（如搜索系统）的关键部分。为了克服这一局限性，本文提出了一种名为ClustCrypt的方法，用于在云中对加密的非结构化大数据进行基于主题的高效聚类。ClustCrypt根据加密数据的统计特征动态估计最佳簇数。它还提供了加密数据的聚类方法。我们在安全的基于云的语义搜索系统（S3BD）的上下文中部署ClustCrypt。在三个数据集上评估ClustCrypt的实验结果表明，集群的一致性平均提高了60%。ClustCrypt还将搜索时间开销减少了78%，并将搜索结果的准确性提高了35%。
Security 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%.