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A linear optimization based method for data privacy in statistical tabular data

Author
Castro, J.; Gonzalez, J.
Type of activity
Report
Date
2017-05-23
Project funding
Very large-scale optimization: methods and applications
Virtual multidisciplinary EnviroNments USing Cloud infrastructures (VENUS-C)
Repository
http://hdl.handle.net/2117/116308 Open in new window
URL
http://www-eio.upc.edu/~jcastro/publications/reports/dr2017-02.pdf Open in new window
Abstract
National Statistical Agencies routinely disseminate large amounts 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 techn...
Citation
Castro, J., Gonzalez, J. "A linear optimization based method for data privacy in statistical tabular data". 2017.
Keywords
benchmarking, data privacy, data science, interior-point methods, lexicographic optimization, linear optimization, statistical disclosure control
Group of research
GNOM - Mathematical Optimization Group

Participants

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