Zhu,H.Liu,H.W.Ou,CarolDavison,R.M.Yang,Z.R.2025-02-012025-02-012017-12Zhu, H, Liu, H W, Ou, C, Davison, R M & Yang, Z R 2017, 'Privacy preserving mechanisms for optimizing cross-organizational collaborative decisions based on the Karmarkar algorithm', Information Systems, vol. 72, pp. 205–217. https://doi.org/10.1016/j.is.2017.10.0080306-4379ORCID: /0000-0001-8190-4009/work/8261330810.1016/j.is.2017.10.008https://hdl.handle.net/20.500.14602/67767Cross-organizational collaborative decision-making involves a great deal of private information which companies are often reluctant to disclose, even when they need to analyze data collaboratively. The lack of effective privacy-preserving mechanisms for optimizing cross-organizational collaborative decisions has become a challenge for both researchers and practitioners. It is even more challenging in the era of big data, since data encryption and decryption inevitably increase the complexity of calculation. In order to address this issue, in this study we introduce the Karmarkar algorithm as a way of dealing with the privacy-preserving distributed linear programming (LP) needed for secure multi-party computation (SMC) and secure two-party computation (STC) in scenarios characterised by mutual distrust and semi-honest participants without the aid of a trusted third party. We conduct two simulations to test the effectiveness and efficiency of the proposed protocols by revising the Karmarkar algorithm. The first simulation indicates that the proposed protocol can obtain the same outcome values compared to no-encryption algorithms. Our second simulation shows that the computational time in the proposed protocol can be reduced, especially for a high-dimensional constraint matrix (e.g., from 100 × 100 to 1000 × 1000). As such, we demonstrate the effectiveness and efficiency that can be achieved in the revised Karmarkar algorithm when it is applied in SMC. The proposed protocols can be used for collaborative optimization as well as privacy protection. Our simulations highlight the efficiency of the proposed protocols for large data sets in particular.enginfo:eu-repo/semantics/closedAccesscollaborative optimizationprivacy preserving mechanismsthe Karmarkar algoruthmsecure multi-party computation (SMC)secure two-party computation (STC)SDG 16 - Peace, Justice and Strong InstitutionsArticleGeneral rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. - Users may download and print one copy of any publication from the public portal for the purpose of private study or research. - You may not further distribute the material or use it for any profit-making activity or commercial gain - You may freely distribute the URL identifying the publication in the public portal" Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.https://www.scopus.com/pages/publications/8503268396118759167https://research.tilburguniversity.edu/en/publications/001f4ee5-bd61-4c2d-a7b0-bb5452a5bfc3(c) Universiteit van TilburgZhu, H.Liu, H.W.Ou, Carol§0000-0001-8190-4009Davison, R.M.Yang, Z.R.