Enhanced controlled tabular adjustment |
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| Affiliation: | 1. Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, United States;2. Department of Anesthesiology, Stanford University School of Medicine, Stanford, CA, United States;3. Palo Alto Institute of Research and Education, Palo Alto, CA, United States;4. Geriatrics Research Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, United States;5. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States;6. Physical Medicine and Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, United States;1. Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Mashhad, Iran;2. Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran, Iran;3. Department of Chemical Engineering, Petroleum University of Technology, Ahwaz, Iran;1. Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI;2. Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL;3. H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine and Harris Health System, Houston, TX;4. Brain Injury Research Center, TIRR Memorial Hermann, Houston, TX;5. Defense and Veterans Brain Injury Center, Walter Reed National Military Medical Center, Bethesda, MD;6. National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD;7. Uniformed Services University of the Health Sciences, Bethesda, MD;8. University of British Columbia, Vancouver, British Columbia, Canada;9. Rehabilitation Institute of Michigan, Department of Psychology and Neuropsychology, Detroit, MI;10. Department of Physical Medicine and Rehabilitation, Wayne State University, Detroit, MI |
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| Abstract: | Statistical agencies collect data from individuals and businesses, and deliver information to the society based on these data. A fundamental feature to consider when releasing information is the “protection” of sensitive values, since too many details could disseminate private information from respondents and therefore violate their rights. Another feature to consider when releasing information is the “utility” to a data user, as a scientist may need this information for research or a politician for making decisions. Clearly the more details there are in the output, the more useful it is, but it is also less protected. This paper discusses a new technique called Enhanced Controlled Tabular Adjustment (ECTA) to ensure that an output is both protected and useful. This technique has been motivated by another approach in the literature of the last decade, and both are compared and evaluated on a set of benchmark instances. |
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| Keywords: | Statistical confidentiality Tabular protection Integer linear programming |
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