Title :
Scalable and Trustworthy Cross-Enterprise WfMSs by Cloud Collaboration
Author :
Gwan-Hwan Hwang ; Yi-Chan Kao ; Yu-Cheng Hsiao
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Normal Univ., Taipei, Taiwan
fDate :
June 27 2013-July 2 2013
Abstract :
Establishing scalable and cross-enterprise workflow management systems (WfMSs) in the cloud requires the adaptation and extension of existing concepts for process management. This paper proposes a scalable and cross-enterprise WfMS with a multitenancy architecture. Especially, it can activate enactment of workflow processes by cloud collaboration. We do not employ the traditional engine-based WfMSs. The key idea is to have the workflow process instance to be self-protected and does not need a workflow engine to secure the data therein. Thus, the process instance discovery and activity execution can be fully independently and distributed. As a result, we can employ the data storage system, Big Table, to store the process instances, which may form a big data. The applying of element-wise encryption and chained digital signature makes it satisfy major security requirements of authentication, confidentiality, data integrity, and nonrepudiation.
Keywords :
cloud computing; cryptography; digital signatures; workflow management software; activity execution; authentication requirement; big table system; chained digital signature; cloud collaboration; confidentiality requirement; cross-enterprise WfMS; data integrity requirement; data storage system; element-wise encryption; multitenancy architecture; nonrepudiation requirement; process instance discovery; process management; security requirement; workflow engine; workflow management system; workflow process enactment; Cloud computing; Digital signatures; Engines; Organizations; Portals; Process control; Servers; Cloud; Multitenancy; WfMS;
Conference_Titel :
Big Data (BigData Congress), 2013 IEEE International Congress on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5006-0
DOI :
10.1109/BigData.Congress.2013.19