scholarly journals Secure cross-domain data sharing architecture for crisis management

Author(s):  
Vaibhav Gowadia ◽  
Enrico Scalavino ◽  
Emil C. Lupu ◽  
Dmitry Starostin ◽  
Alexey Orlov
Author(s):  
Parminder Singh ◽  
Mehedi Masud ◽  
M. Shamim Hossain ◽  
Avinash Kaur

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhao Hongmei

In the current cross-border electronic commerce (e-commerce) system, various document recording and authorization processes are cumbersome, record sharing efficiency is low, and identity verification is difficult. A method of asymmetric encryption technology combining Blockchain technology and cryptography is proposed. The advantages of asymmetric encrypted communications include high security and ease of multiparties communication collaboration, being applied to a peer-to-peer network formed by Blockchain technology, and making cross-border e-commerce record cross-domain sharing traceable, data immutable, and identity verification simplified. First of all, based on the immutable modification of Blockchain technology and asymmetric encryption technology, file synchronization contracts and authorization contracts are designed. Its distributed storage advantages ensure the privacy of users’ cross-border e-commerce information. Second, the design of the cross-domain acquisition contract can effectively verify the identity and transmission efficiency of both parties to the data sharing, so that illegal users can be safely filtered without a third-party notary institution. The simulation experiment results show that the solution proposed in this paper has obvious advantages in data antitheft, multiparty authentication, and saving system overhead compared with traditional cloud computing methods to solve the problem of sharing medical records. It provides a reference for solving the security problems in the process of data sharing by using the advantages of Blockchain’s decentralization and auditability and provides reference ideas for solving the problems of data sharing and cross-domain authentication.


Author(s):  
Kai Fan ◽  
Qiang Pan ◽  
Junxiong Wang ◽  
Tingting Liu ◽  
Hui Li ◽  
...  

Author(s):  
R. Roller ◽  
J. Roes ◽  
E. Verbree

Floodings represent a permanent risk to the Netherlands in general and to her power supply in particular. Data sharing is essential within this crisis scenario as a power cut affects a great variety of interdependant sectors. Currently used data sharing systems have been shown to hamper interoperability between stakeholders since they lack flexibility and there is no consensus in term definitions and interpretations. The study presented in this paper addresses these challenges by proposing a new data sharing solution based on Linked Data, a method of interlinking data points in a structured way on the web. A conceptual model for two data sharing parties in a flood-caused power cut crisis management scenario was developed to which relevant data were linked. The analysis revealed that the presented data sharing solution burderns its user with extra costs in the short run, but saves resources in the long run by overcoming interoperability problems of the legacy systems. The more stakeholders adopt Linked Data the stronger its benefits for data sharing will become.


2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Chen Gao ◽  
Yong Li ◽  
Fuli Feng ◽  
Xiangning Chen ◽  
Kai Zhao ◽  
...  

Web systems that provide the same functionality usually share a certain amount of items. This makes it possible to combine data from different websites to improve recommendation quality, known as the cross-domain recommendation task. Despite many research efforts on this task, the main drawback is that they largely assume the data of different systems can be fully shared . Such an assumption is unrealistic different systems are typically operated by different companies, and it may violate business privacy policy to directly share user behavior data since it is highly sensitive. In this work, we consider a more practical scenario to perform cross-domain recommendation. To avoid the leak of user privacy during the data sharing process, we consider sharing only the information of the item side, rather than user behavior data. Specifically, we transfer the item embeddings across domains, making it easier for two companies to reach a consensus (e.g., legal policy) on data sharing since the data to be shared is user-irrelevant and has no explicit semantics. To distill useful signals from transferred item embeddings, we rely on the strong representation power of neural networks and develop a new method named as NATR (short for N eural A ttentive T ransfer R ecommendation ). We perform extensive experiments on two real-world datasets, demonstrating that NATR achieves similar or even better performance than traditional cross-domain recommendation methods that directly share user-relevant data. Further insights are provided on the efficacy of NATR in using the transferred item embeddings to alleviate the data sparsity issue.


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