scholarly journals Privacy-Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Bin Wu ◽  
Xianyi Chen ◽  
Zongda Wu ◽  
Zhiqiang Zhao ◽  
Zhuolin Mei ◽  
...  

The arrival of cloud computing age makes data outsourcing an important and convenient application. More and more individuals and organizations outsource large amounts of graph data to the cloud computing platform (CCP) for the sake of saving cost. As the server on CCP is not completely honest and trustworthy, the outsourcing graph data are usually encrypted before they are sent to CCP. The optimal route finding on graph data is a popular operation which is frequently used in many fields. The optimal route finding with support for semantic search has stronger query capabilities, and a consumer can use similar words of graph vertices as query terms to implement optimal route finding. Due to encrypting the outsourcing graph data before they are sent to CCP, it is not easy for data customers to manipulate and further use the encrypted graph data. In this paper, we present a solution to execute privacy-guarding optimal route finding with support for semantic search on the encrypted graph in the cloud computing scenario (PORF). We designed a scheme by building secure query index to implement optimal route finding with support for semantic search based on searchable encryption idea and stemmer mechanism. We give formal security analysis for our scheme. We also analyze the efficiency of our scheme through the experimental evaluation.

2012 ◽  
Vol 35 (6) ◽  
pp. 1262 ◽  
Author(s):  
Ke-Jiang YE ◽  
Zhao-Hui WU ◽  
Xiao-Hong JIANG ◽  
Qin-Ming HE

2020 ◽  
Vol 29 (2) ◽  
pp. 1-24
Author(s):  
Yangguang Li ◽  
Zhen Ming (Jack) Jiang ◽  
Heng Li ◽  
Ahmed E. Hassan ◽  
Cheng He ◽  
...  

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Hanke ◽  
Franco Pestilli ◽  
Adina S. Wagner ◽  
Christopher J. Markiewicz ◽  
Jean-Baptiste Poline ◽  
...  

Abstract Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.


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