Multi Keyword Search on Encrypted Data with Ranking

To maintain the anonymity of users, cloud storage owners often outsource encrypted documents. As a consequence, it is important to establish efficient and precise cypher text search techniques. One issue would be that the connection between documents is typically obscured during the encryption process, resulting in a significant deterioration of search accuracy efficiency. Additionally, the volume of data stored in data centers has exploded. This will make it significantly more difficult to create cipher text search schemes capable of providing efficient and reliable online information retrieval on large quantities of encrypted data. The paper proposes a hierarchical clustering approach in order to accommodate additional search semantics and to satisfy the demand for fast cipher text search in a big data environment. The proposed hierarchical approach clusters documents according to their minimum importance levels and then sub-clusters them until the maximum cluster size is reached. This approach can achieve linear computational complexity throughout the search process, spite of the fact that its size of the record set grows exponentially. The minimum hash sub-tree structure is used in this paper to check the validity of search results. The results demonstrate that as the number of documents in the dataset increases, the proposed method's search time increases linearly, while the conventional method's search time increases exponentially. Additionally, the suggested method outperforms the standard method in terms of rank privacy and document relevance.