Abstract: Cloud computing means saving and retrieving data and programs over the internet instead of your computer's hard drive. The cloud is just a representative for the Internet. It goes back to the days of flowcharts and presentations that would represent the huge server-farm infrastructure of the Internet as nothing but a puffy, white cumulus cloud, accepting connections and distributing out information as it floats. Cloud computing has become a social fact used by most people every day. As with every important social fact there are issues that limit its widespread adoption. Most issues start from the fact that the user loses control of his or her data, because it is stored on a computer belonging to someone else. This happens when the owner of the remote servers is a person or organization other than the user; as their interests may point in different directions. In this paper, a secure multi-keyword ranked search scheme on encrypted cloud data is devised along with dynamic update operations of documents. Scrupulously, the vector space model and the extensively-used TF x IDF model are combined in the index construction and query generation. The special tree-based index structure is formed and propose a Greedy Depth-first Search algorithm to make available efficient multi-keyword ranked search. The secure kNN algorithm is effectively used to encrypt the index and query vectors, and meanwhile provide accurate relevance score calculation between encrypted index and query vectors. In order to withstand statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the updating of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.
Keywords: Cloud data, Dynamic Policy, Greedy Algorithm, Multi-Keyword Search, Security.