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On achieving secure collaboration in supply chains

Author

Listed:
  • Sharad Barkataki

    (American University in Dubai)

  • Hassan Zeineddine

    (American University in Dubai)

Abstract

Security of private information is a critical requirement for information sharing in supply chains. Security is measured on a variety of metrics: participating suppliers in a supply chain process need guarantees on confidentiality, anonymity, and privacy; the purchaser needs assurance on verifiability and non-repudiation. Given the mutually exclusive nature of security metrics such as anonymity vs. non-repudiation, and confidentiality vs. verifiability, the challenge is to design a process that satisfies all these metrics. In this paper, we propose three major processes that enable secure information sharing and secure computation of arbitrary supply chain functions. We evaluate and compare the secure processes using a ranking method and score function, which we propose for that purpose. We find that certain processes will be preferred over other processes, depending on the environmental characteristics and user preferences. Thus, our secure processes aim to solve major collaboration issues in supply chains.

Suggested Citation

  • Sharad Barkataki & Hassan Zeineddine, 2015. "On achieving secure collaboration in supply chains," Information Systems Frontiers, Springer, vol. 17(3), pages 691-705, June.
  • Handle: RePEc:spr:infosf:v:17:y:2015:i:3:d:10.1007_s10796-013-9448-3
    DOI: 10.1007/s10796-013-9448-3
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    References listed on IDEAS

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    Cited by:

    1. Ajaya Kumar Swain & Ray Qing Cao, 2019. "Using sentiment analysis to improve supply chain intelligence," Information Systems Frontiers, Springer, vol. 21(2), pages 469-484, April.
    2. Yasushi Ueki & Masatsugu Tsuji & Nuchjarin Intalar & Somrote Komolavanij, 2023. "Effects of Quality Control Activities and Customers’ ICT Investment on Digitalization in Manufacturing Firms in Southeast Asia," The Review of Socionetwork Strategies, Springer, vol. 17(1), pages 25-53, April.
    3. Fabian Taigel & Anselme K. Tueno & Richard Pibernik, 2018. "Privacy-preserving condition-based forecasting using machine learning," Journal of Business Economics, Springer, vol. 88(5), pages 563-592, July.

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