Survey on Multi keyword Ranked Search Scheme over Encrypted Data

Abstract

In recent years, the advancements and the fame of cloud computing are increasing which is actuating the data owners to keep their personal and professional data on public cloud servers like Amazon, Microsoft, Google, Apple, etc with the help of data outsourcing. The other advantage of outsourcing the data over cloud servers is for high benefit and lesser cost in managing the data and the data can be accessed from anywhere and at any time. However, for privacy concerns, the data that are highly sensitive should be encrypted before outsourcing. Taking into consideration the huge amount of data users and files that are present in the cloud, it is important that multiple keywords should be allowed in the searching request and retrieve the files relevant to those keywords. There are some methods and solutions offered to provide privacy and security for the data over the cloud server. Since the document vector's dimension is equal to the dictionary's size, traditional searchable encryption schemes based on the bag-of-words model require a lot of space to store the document set's index. The bag-of-words model often ignores semantic information between keywords and documents, resulting in potentially meaningless search results for users. The natural language processing (NLP) model can be used as it extracts document features from word and paragraph context information. The features can be used to assess document similarity and provide latent semantics information. The NLP model was used to construct a semantic-conscious multi keyword graded search scheme in this survey on dynamic semantic aware multi keyword ranked search.