scholarly journals Toward Privacy-Assured Cloud Data Services with Flexible Search Functionalities

Author(s):  
Ming Li ◽  
Shucheng Yu ◽  
Wenjing Lou ◽  
Y. Thomas Hou
Keyword(s):  
Author(s):  
Ankita Puri ◽  
Naveen Kumari

Day by Day ,with the advancement of modern technology over cloud computing motivating the data owners to outsource their data to the cloud server like Amazon, Microsoft, Azure etc .With the help of data outsourcing ,the organization can provide reliable data services to their user without any management of the overhead concern. Suppose, a large number of users that are on cloud and large number of documents on cloud, Its important for the service provider to allow multi-keyword query and provided the result that meet efficient data retrieval needs. In this paper, for the first time, we define and solve the challenging problem of privacy preserving multi-keyword ranked search over encrypted cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Among various multi-keyword semantics, we choose the efficient principle of “coordinate matching”, i.e., as many matches as possible, to capture the similarity between search query and data documents, and further use “inner product similarity” to quantitatively formalize such principle for similarity measurement.


2021 ◽  
Author(s):  
Vivek Narasayya ◽  
Surajit Chaudhuri
Keyword(s):  

Author(s):  
M. Shaheda Begum

Abstract: Motivated by the exponential growth and the huge success of cloud data services bring the cloud common place for data to be not only stored in the cloud, but also shared across multiple users. Our scheme also has the added feature of access control in which only valid users are able to decrypt the stored information. Unfortunately, the integrity of cloud data is subject to skepticism due to the existence of hardware/software failures and human errors. Several mechanisms have been designed to allow both data owners and public verifiers to efficiently audit cloud data integrity without retrieving the entire data from the cloud server. However, public auditing on the integrity of shared data with these existing mechanisms will inevitably reveal confidential information—identity privacy—to public verifiers. In this paper, we propose a novel privacy-preserving mechanism that supports public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute verification metadata needed to audit the correctness of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from public verifiers, who are able to efficiently verify shared data integrity without retrieving the entire file. In addition, our mechanism is able to perform multiple auditing tasks simultaneously instead of verifying them one by one. Our experimental results demonstrate the effectiveness and efficiency of our mechanism when auditing shared data integrity. Keywords: Public auditing, privacy-preserving, shared data, cloud computing


Author(s):  
Ankita Puri ◽  
Naveen Kumari

Day by Day ,with the advancement of modern technology over cloud computing motivating the data owners to outsource their data to the cloud server like Amazon, Microsoft, Azure etc .With the help of data outsourcing ,the organization can provide reliable data services to their user without any management of the overhead concern. Suppose, a large number of users that are on cloud and large number of documents on cloud, Its important for the service provider to allow multi-keyword query and provided the result that meet efficient data retrieval needs. In this paper, for the first time, we define and solve the challenging problem of privacy preserving multi-keyword ranked search over encrypted cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Among various multi-keyword semantics, we choose the efficient principle of “coordinate matching”, i.e., as many matches as possible, to capture the similarity between search query and data documents, and further use “inner product similarity” to quantitatively formalize such principle for similarity measurement.


2016 ◽  
Vol 49 (1) ◽  
pp. 1-39 ◽  
Author(s):  
Jun Tang ◽  
Yong Cui ◽  
Qi Li ◽  
Kui Ren ◽  
Jiangchuan Liu ◽  
...  

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