Privacy-preserving linear programming

  • Authors:
  • Jaideep Vaidya

  • Affiliations:
  • Rutgers University, Newark, NJ

  • Venue:
  • Proceedings of the 2009 ACM symposium on Applied Computing
  • Year:
  • 2009

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Abstract

With the rapid increase in computing, storage and networking resources, data is not only collected and stored, but also analyzed. This creates a serious privacy problem which often inhibits the use of this data. In this paper, we focus on the problem of linear programming, which is the most important sub-class of optimization problems. We consider the case where the objective function and the constraints are partitioned between two parties with one party holding the objective while the other holds the constraints. We propose a very efficient and secure transformation based solution that has the significant added benefit of being independent of the specific linear programming algorithm used.