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A Privacy Preserving Approach to Collaborative Systemic Risk Identification

The Use-case of Supply Chain Networks
: Zare Garizy, Tirazheh; Fridgen, Gilbert; Wederhake, Lars

Volltext ()

Security and Communication Networks (2018), Article 3858592, 18 S.
ISSN: 1939-0122
ISSN: 1939-0114
Bundesministerium für Bildung und Forschung BMBF
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer FIT ()
Multiparty Computation; Algorithms; Privacy Preservation; Supply Chain Network; Systemic Risk; Risk Management

Globalization, and outsourcing are two main factors which are leading to higher complexity of supply chain networks. Due to the strategic importance of having a sustainable network it is necessary to have an enhanced supply chain network risk management. In a supply chain network many firms depend directly or indirectly on a specific supplier. In this regard, unknown risks of networks structure can endanger the whole supply chain networks robustness. In spite of the importance of risk identification of supply chain network, firms are not willing to exchange the structural information of their network. Firms are concerned about risking their strategic positioning or established connections in the network. The paper proposes to combine secure multiparty computation cryptography methods with risk identification algorithms from social network analysis to address this challenge. The combination enables structural risk identification of supply chain networks without endangering firms competitive advantage.