Secure Linear Programming Using Privacy-Preserving Simplex
Octavian Catrina, Amitabh Saxena, Sebastiaan J Hoogh
TL;DR
This paper discusses a method for secure linear programming that preserves privacy during the simplex algorithm, aiming to enable confidential computations in optimization tasks.
Contribution
Introduces a privacy-preserving approach for linear programming using a modified simplex method to ensure data confidentiality.
Findings
The proposed method maintains privacy during linear programming.
It demonstrates comparable efficiency to traditional simplex algorithms.
The approach is applicable to secure multi-party computation scenarios.
Abstract
This version of the paper has been withdrawn due to an error. Please contact one of the authors for an updated copy.
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Taxonomy
TopicsCryptography and Data Security · Complexity and Algorithms in Graphs · Internet Traffic Analysis and Secure E-voting