TY - GEN
T1 - Preserving privacy in collaborative business process composition
AU - Irshad, Hassaan
AU - Shafiq, Basit
AU - Vaidya, Jaideep
AU - Bashir, Muhammad Ahmed
AU - Shamail, Shafay
AU - Adam, Nabil
N1 - Publisher Copyright:
© Copyright 2015 SCITEPRESS - Science and Technology Publications. All rights reserved.
PY - 2015
Y1 - 2015
N2 - Collaborative business process composition exploits the knowledge of existing business processes of related organizations to compose an executable business process for a given organization based on its requirements and design specifications. Typically, this requires organizations to share and upload their existing business process execution sequences to a central repository. However, even after masking of confidential data, the execution sequences may still include sensitive business information which organizations may not want to share with their competitors. To address this issue, we develop a privacy-preserving Business Process Recommendation and Composition System (BPRCS), that generates a differentially private dataset of execution sequences which can be published and shared with other organizations for composition and implementation of their business processes. We also employ process mining and classification techniques on this differentially private dataset to regenerate the executable business process workflow. We experimentally validate the effectiveness of our approach.
AB - Collaborative business process composition exploits the knowledge of existing business processes of related organizations to compose an executable business process for a given organization based on its requirements and design specifications. Typically, this requires organizations to share and upload their existing business process execution sequences to a central repository. However, even after masking of confidential data, the execution sequences may still include sensitive business information which organizations may not want to share with their competitors. To address this issue, we develop a privacy-preserving Business Process Recommendation and Composition System (BPRCS), that generates a differentially private dataset of execution sequences which can be published and shared with other organizations for composition and implementation of their business processes. We also employ process mining and classification techniques on this differentially private dataset to regenerate the executable business process workflow. We experimentally validate the effectiveness of our approach.
KW - Business process composition
KW - Privacy
UR - http://www.scopus.com/inward/record.url?scp=84964950985&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964950985&partnerID=8YFLogxK
U2 - 10.5220/0005567801120123
DO - 10.5220/0005567801120123
M3 - Conference contribution
AN - SCOPUS:84964950985
T3 - SECRYPT 2015 - 12th International Conference on Security and Cryptography, Proceedings; Part of 12th International Joint Conference on e-Business and Telecommunications, ICETE 2015
SP - 112
EP - 123
BT - SECRYPT 2015 - 12th International Conference on Security and Cryptography, Proceedings; Part of 12th International Joint Conference on e-Business and Telecommunications, ICETE 2015
A2 - Obaidat, Mohammad S.
A2 - Lorenz, Pascal
A2 - Samarati, Pierangela
PB - SciTePress
T2 - 12th International Conference on Security and Cryptography, SECRYPT 2015
Y2 - 20 July 2015 through 22 July 2015
ER -