Practical Privacy-Preserving Multiparty Linear Programming Based on Problem Transformation

Jannik Dreier 1 Florian Kerschbaum 2
1 CASSIS - Combination of approaches to the security of infinite states systems
FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174), Inria Nancy - Grand Est, LORIA - FM - Department of Formal Methods
Abstract : Cryptographic solutions to privacy-preserving multi-party linear programming are slow. This makes them unsuitable for many economically important applications, such as supply chain optimization, whose size exceeds their practically feasible input range. In this paper we present a privacy-preserving transformation that allows secure outsourcing of the linear program computation in an efficient manner. We evaluate security by quantifying the leakage about the input after the transformation and present implementation results. Using this transformation, we can mostly replace the costly cryptographic operations and securely solve problems several orders of magnitude larger.
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Jannik Dreier, Florian Kerschbaum. Practical Privacy-Preserving Multiparty Linear Programming Based on Problem Transformation. Third IEEE International Conference on Information Privacy, Security, Risk and Trust (PASSAT'11), IEEE, Oct 2011, Boston, United States. ⟨10.1109/PASSAT/SocialCom.2011.19⟩. ⟨hal-01338043⟩



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