The keyword searching algorithm plays a vital role in cloud computing Architecture. Here, the searching technique is applied as a tool to encrypted data set which is outsourced. This technique works well, by either directed on multi keyword findings with exact match or one keyword search with fuzzy logic with a result of true or false. Though the existing techniques come across sensible difficultyin execution upon the encrypted data set, in the execution of multiple keyword based searching, keyword correction is difficult to achieve. To overcome with this problem, we construct a highly efficient, multiple corrections in keyword searching algorithm using ranking scheme. Multiple corrections like spell checks and errors. In the existing system of Author Wang and others scheme, they were unable to address the aforementioned problems, But in our method of multiple keyword searching technique, the Fuzzy Gramm algorithm helps to detect and handle the spelling errors by making a function call. Additionally the keywords with similar ancestry can be found by making use of stemming procedure. Alongside with thatrank keyword is used to achieve accurate checking with matched text file content. Thus the proposed approach enhances security and reliability to the maximum extent compared to the existing system.

https://iopscience.iop.org/article/10.1088/1757-899X/1084/1/012103/pdf

An Efficient Privacy-Preserving Ranked Keyword Search Method

. Nowadays due to the popularity of clouds and their quality services, many of the users such as organizations, industries as well as individuals are migrating towards the cloud to store their important, confidential data and get easy access to their data anywhere anytime over the internet. Different encryption techniques are employed in cloud computing to ensure data confidentiality, security, and privacy. This makes it harder for end-users to retrieve precise data. Due to the huge amount of data over the cloud and multiple data users, secure data storage and retrieval are required. So in this paper develop an efficient data group sharing and multi-keyword ranked search method for encrypted cloud data collection in this research work. The developed system is implemented using the El-Gamal cryptography algorithm to provide security through effective key generation techniques and encryption strategy. Here, a multi-owner data setting is used instead of a centralized data owner setting; each member of the system in one particular group gets equal rights for both searching and sharing functionality and this may increase system usability. By taking into consideration lots of data in the cloud, the vector space model and TF-IDF model are utilized and according to the cosine similarity score, the method generates a ranked multi-keyword search result to deliver effective query result from numerous data and enhance secrecy in the situation of numerous data owners. In this system searching efficiency is improved by developing an index-based search structure. In the group, data can disperse with co-owners/users by developing a role-based access policy (RBAP), and a user revocation strategy is developed with low computation time as well as communication overhead. At last, the efficiency and security of a developed system are exhibited by broad exploratory assessment.

Efficient and Secure Multi-Keyword Ranked Search and Group Data Sharing for Encrypted Cloud Data

Through the advancement of cloud storage, data providers are allowed to outsource their complicated data processing processes with considerable versatility and economic benefits since local locations to the private, public cloud. However, confidential data must be secured before outsourcing in order for data to be safe, which obsolescent common usage of data relying on plaintext search. Therefore, it is incredibly necessary to allow a secure cloud data search service Given the vast number of web users and cloud records, in order to be applicable to such keywords, several keywords should be acknowledged in the search request and return records. The searchable encryption-based research specializes in search of a particular keyword or in boolean keywords, seldom processing the outcomes of pursuit.  For the very first time, the problem of protecting the privacy of multi - keyword search for encrypted data in cloud storage is described as well as solved in this article. For such a safe cloud data usage scheme, we series a series of strict privacy criteria. We pick from the different multi-keyword semantics the effective "coordinate matching" similarity test, i.e. as various matches as likely, in order to catch the importance of the data papers to the search. We can use "inner product similitude" to test this resemblance attribute quantitatively. Initial, on the basis of stable internal commodity estimation and then, in two separate Vulnerability Models, we suggest a simple concept for the MRSE to reach multiple rigorous data protection standards. We expand these two frameworks to help further search terminology to improve our user experience for the data search service. Examination of data security and assurance of performance of suggested systems shall be carried out in-depth. Experiments in the real-world data set indicate that plans for numerical and connectivity structures effectively add small overheads.

Multi-Keyword Privacy Security Protected Search Through Encrypted Data On Cloud Storage

In medical cloud computing, a patient can send her medical data to a cloud server from afar. Because medical data is highly sensitive, only authorized doctors are allowed to access it in this case. A frequent solution is to encrypt data before outsourcing it, with the patient simply sending the corresponding encryption key to the authorized doctors. However, due to the difficulties of digging through the encrypted data, the usability of outsourced medical data is severely limited. Over medical cloud data, we propose Secure and Efficient Dynamic Searchable Symmetric Encryption (SEDSSE) schemes. To begin, we propose a dynamic searchable symmetric encryption scheme that uses the secure k-Nearest Neighbor (kNN) and Attribute-Based Encryption (ABE) techniques to achieve two important security features: forward privacy and backward privacy, both of which are difficult to achieve in the field of dynamic searchable symmetric encryption. Then, to address the key sharing problem that plagues the kNN-based searchable encryption strategy, we suggest an improved technique. In terms of storage, search, and update complexity, our solutions outperform prior proposals. Extensive tests show that our approaches are efficient in terms of storage overhead, index building, trapdoor generation, and query.

Achieving Secure and Efficient Dynamic Searchable Symmetric Encryption over Medical Cloud Data

With the rapid development of cloud computing, an increasing number of data owners are motivated to outsource their sensitive data to cloud servers for flexibility and reduced cost in data management. However, privacy is a big concern for outsourcing data to the cloud, particularly for data sets like health records and financial records which usually contain sensitive information. A practical and widely used technique for data privacy preservation is to encrypt data before outsourcing to the cloud servers. In this case, the retrieval of required files from the encrypted cloud becomes a problem which requires searching over the encrypted data. In this paper, we propose an efficient multi-keyword ranked search scheme over encrypted data in cloud using the data structure group B+ tree. To improve the query efficiency, we construct a B+ tree index structure based on the group of data sets, which can optimize the index structure and provide efficient and fast relevance between the query and cloud data. Specifically, for the privacy concern of query data, we use the improved KNN-based algorithm to encrypt sensitive data; the searchable encryption of this scheme achieves accuracy multi-keyword query over encrypted cloud data and returns the highest relevant top-k results. Extensive experimental results on real-life data sets demonstrate that the proposed approach can significantly reduce the index storage and improve the retrieval efficiency.

Jian Xu

Papers

An Efficient Multi-keyword top-k Search Scheme over Encrypted Cloud Data