  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%
