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A Privacy-Sensitive Collaborative Approach to Business Process Development

  • Hassaan Irshad
  • Basit Shafiq
  • Jaideep VaidyaEmail author
  • Muhammad Ahmed Bashir
  • Hafiz Salman Asif
  • Sameera Ghayyur
  • Shafay Shamail
  • Adam Nabil
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 585)

Abstract

The objective of this paper is to enable organizations to generate an executable business process from high level design specifications. The basic idea is to exploit the knowledge of the existing business processes of related organizations to generate an executable business process for the given organization based on its requirements. However, this requires organizations with existing business processes to share their process execution sequences. Since the execution sequences (even after data sanitization) still include sensitive business information which organizations may not want to share with their competitors, this needs to be done in a privacy-sensitive way.

Towards this, we propose a privacy preserving approach for generating a repository of business process execution sequences. The proposed approach is based on differential privacy and does not reveal any sensitive information about individual organizations. The proposed approach is designed for a collaborative environment in which organizations share their business process details with a trusted third party called Business Process Recommendation and Composition System (BPRCS). BPRCS generates a differentially private dataset of execution sequences and employs process mining and classification techniques on this dataset to regenerate the executable business process workflow. We have implemented a prototype of BPRCS in J2EE and used it to validate the approach.

Keywords

Business Process Dependency Graph Execution Sequence Minimum Support Threshold Differential Privacy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The work of Shafiq and Shamail is supported by the LUMS Faculty Initiative Fund Grant and by HEC under the PAK-US Science and Technology Cooperation Program. The work of Vaidya is supported by the NSF under Grant No. CNS-1422501. The work of Adam is supported by the National Academies of Sciences, Engineering, and Medicine under the PAK-US Science and Technology Cooperation Program.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hassaan Irshad
    • 1
  • Basit Shafiq
    • 1
  • Jaideep Vaidya
    • 2
    Email author
  • Muhammad Ahmed Bashir
    • 1
  • Hafiz Salman Asif
    • 2
  • Sameera Ghayyur
    • 1
  • Shafay Shamail
    • 1
  • Adam Nabil
    • 2
  1. 1.Lahore University of Management SciencesLahorePakistan
  2. 2.CIMICRutgers UniversityNewarkUSA

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