A Project Report on “AN EFFICIENT PRIVACY-PRESERVING RANKED KEYWORD SEARCH METHOD”
Loading...
Files
Date
2019-09-11T12:11:25Z
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
AS we step into the big data era, terabyte of data are produced world-wide per day. Enterprises
and users who own a large amount of data usually choose to outsource their precious data to
cloud facility in order to reduce data management cost and storage facility spending. As a
result, data volume in cloud storage facilities is experiencing a dramatic increase. Although
cloud server providers (CSPs) claim that their cloud service is armed with strong security
measures, security and privacy are major obstacles preventing the wider acceptance of cloud
computing service.
A traditional way to reduce information leakage is data encryption. However, this will make
server-side data utilization, such as searching on encrypted data, become a very challenging
task. In the recent years, researchers have proposed many ciphertext search schemes [35-
38][43] by incorporating the cryptography techniques. These methods have been proven with
provable security, but their methods need massive operations and have high time complexity.
Therefore, former methods are not suitable for the big data scenario where data volume is very
big and applications require online data processing. In addition, the relationship between
documents is concealed in them above methods. The relationship between documents
represents the properties of the documents and hence maintaining the relationship is vital to
fully express a document. For example, the relationship can be used to express its category. If
a document is independent of any other documents except those documents that are related to
sports, then it is easy for us to assert this document belongs to the category of the sports.
In this paper, a vector space model is used and every document is represented by a vector,
which means every document can be seen as a point in a high dimensional space. Due to the
relationship between different documents, all the documents can be divided into several
categories. In other words, the points whose distance are short in the high dimensional Space
can be classified into a specific category. The search time can be largely reduced by selecting
the desired category and abandoning the irrelevant categories. Comparing with all the
documents in the dataset, the number of documents which user aims at is very small. Due to
the small number of the desired documents, a specific category can be further divided into
several sub-categories. Instead of using the traditional sequence search method, a backtracking
algorithm is produced to search the target documents
Description
Keywords
16VFSB7062, Swetha C, AN EFFICIENT PRIVACY-PRESERVING RANKED KEYWORD SEARCH METHOD”, BCA Project Reports