A hierarchical clustering method for big data oriented ciphertext search

Following the wide use of cloud services, the volume of data stored in the data center has experienced a dramatically growth which makes real-time information retrieval much more difficult than before. Furthermore, text information is usually encrypted before being outsourced to data centers in order to protect users´ data privacy. Current techniques to search on encrypted data do not perform well within such a massive data environment. In this paper, a hierarchical clustering method for ciphertext search within a big data environment is proposed. The proposed approach clusters the documents based on the minimum similarity threshold, and then partitions the resultant clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against exponential size of document collection. In addition, retrieved documents have a better relationship with each other than traditional methods. An experiment has been conducted using the collection set built from the recent ten years´ IEEE INFOCOM publications, including about 3000 documents with nearly 5300 keywords. The results have validated our proposed approach.