# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 6 Conclusions  
In this work, we presented an approach toward solving the collaborative freighter delivery problem in a fully confidentiality-preserving manner based on secure multi-party computation. Our prototypical implementation in MPyC shows that the problem can be solved in a reasonable time for sufficiently large instance sizes to realize the full potential of collaborative planning. By re-using modeling, parameters, and problem instances from previous work [3], we were able to show that our approach achieves the same quality of solutions as its plaintext alternative.  
We expect that the efficiency can be increased substantially by applying further optimizations or by switching the underlying MPC framework to MP-SPDZ [41], which has been shown to outperform MPyC in many situations [48].  
Similar to previous work, our modeling faces limitations when applied to real-world scenarios, particularly with respect to timing aspects. To address this, we outlined a broad range of potential future extensions and provided indications of their technical feasibility. While some enhancements could be integrated with relatively low effort, others may require substantial research and engineering to achieve practical efficiency. However, we believe that, in the long term, the suggested approach indicates a potential path toward reducing the number of empty runs in logistics and increasing the average load factor of trucks, thereby reducing the carbon footprint of the sector and reducing costs.  
**Author Contributions:** The authors contributed to the different phases of this work as follows: Conceptualization: G.S., S.K., E.T., C.P. and E.W.; methodology: G.S., S.K., E.T., C.P. and E.W.; software: G.S. and E.W.; validation: G.S. and E.W.; formal analysis: G.S. and S.K.; investigation: G.S., S.K. and C.P.; resources: G.S.; data curation: G.S., E.T. and E.W.; writing--original draft preparation: [PERSON] and S.K.; writing--review and editing: [PERSON], S.K. and E.W.; visualization: G.S., S.K. and E.T.; supervision: E.T.; project administration: C.P.; funding acquisition: S.K., E.T. and C.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was in parts funded by the Austrian Research Promotion Agency (FFG) under grant agreement number 4790430 (mupol).  
**Data Availability Statement:** The dataset used in the validation section is the same used by [PERSON] et al. [3] and is described in [5]. The source code of the presented MPC solution is available at [[https://github.com/ait-crypto/MUPOL-MPC](https://github.com/ait-crypto/MUPOL-MPC)]([https://github.com/ait-crypto/MUPOL-MPC](https://github.com/ait-crypto/MUPOL-MPC)) (accessed on 30 June 2025). The plaintext version from [3] serving as a baseline for our validation can be accessed at [[https://github.com/ait-crypto/MUPOL-Plaintext](https://github.com/ait-crypto/MUPOL-Plaintext)]([https://github.com/ait-crypto/MUPOL-Plaintext](https://github.com/ait-crypto/MUPOL-Plaintext)) (accessed on 30 June 2025).  
**Acknowledgments:** The authors would like to thank [PERSON] for reviewing the source code developed as part of this work and providing valuable feedback.  
**Conflicts of Interest:** The authors declare no conflicts of interest.