A linear optimization-based method for data privacy in statistical tabular data. (2nd January 2019)

• Record Type: Journal Article
• Authors: Castro, Jordi; González, José A.
• Is Part Of: Optimization methods and software. Volume 34:Number 1(2019)
• Page Start: 37
• Page End: 61
• Publication Date: 2019-01-02
• Subjects: linear optimization -- interior-point methods -- benchmarking -- lexicographic optimization -- data science -- data privacy -- statistical disclosure control; 90C05 -- 90C29 -- 90C51 -- 90C90; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2017.1332620
• Languages: English
• ISSNs: 1055-6788

Abstract

Abstract : National Statistical Agencies routinely disseminate large amount of data. Prior to dissemination these data have to be protected to avoid releasing confidential information. Controlled tabular adjustment (CTA) is one of the available methods for this purpose. CTA formulates an optimization problem that looks for the safe table which is closest to the original one. The standard CTA approach results in a mixed integer linear optimization (MILO) problem, which is very challenging for current technology. In this work we present a much less costly variant of CTA that formulates a multiobjective linear optimization (LO) problem, where binary variables are pre-fixed, and the resulting continuous problem is solved by lexicographic optimization. Extensive computational results are reported using both commercial (CPLEX and XPRESS) and open source (Clp) solvers, with either simplex or interior-point methods, on a set of real instances. Most instances were successfully solved with the LO-CTA variant in less than one hour, while many of them are computationally very expensive with the MILO-CTA formulation. The interior-point method outperformed simplex in this particular application.