Privacy-Preserving Horizontally Partitioned Linear Programs
Abstract
We propose a simple privacy-preserving reformulation of a linear program whose equality constraint
matrix is partitioned into groups of rows. Each group of matrix rows and its corresponding right hand side
vector are owned by a distinct private entity that is unwilling to share ormake public its row group or right hand
side vector. By multiplying each privately held constraint group by an appropriately generated and privately
held random matrix, the original linear program is transformed into an equivalent one that does not reveal any
of the privately held data or make it public. The solution vector of the transformed secure linear program is
publicly generated and is available to all entities.
Subject
horizontally partitioned data
linear programming
privacy-preserving
security
Permanent Link
http://digital.library.wisc.edu/1793/64354Citation
10-02