Abstract
IPUMS-International disseminates more than two hundred-fifty integrated, confidentialized census microdata samples to thousands of researchers world-wide at no cost. The number of samples is increasing at the rate of several dozen per year, as quickly as the task of integrating metadata and microdata is completed. Protecting the statistical confidentiality and privacy of individuals represented in the microdata is a sine qua non of the IPUMS project. For the 2010 round of censuses, even greater protections are required, while researchers are demanding ever higher precision and utility. This paper describes a tripartite collaborative experiment using a ten percent household sample of the 2011 census of Ireland to estimate risk, mask the microdata using controlled shuffling, and assess analytical utility by comparing the masked data against the unprotected source microdata. Controlled shuffling exploits hierarchically ordered coding schemes to protect privacy and enhance utility. With controlled shuffling, the lesson seems to be the more detail means less risk and greater utility. Overall, despite substantial perturbation of the masked dataset (30% of adults on one or more characteristic), we find that data utility is very high and information loss is slight, even for fairly complex analytical problems.
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References
Cleveland, L., McCaa, R., Ruggles, S., Sobek, M.: When Excessive Perturbation Goes Wrong and Why IPUMS-International Relies Instead on Sampling, Suppression, Swapping, and Other Minimally Harmful Methods to Protect Privacy of Census Microdata. In: Domingo-Ferrer, J., Tinnirello, I. (eds.) PSD 2012. LNCS, vol. 7556, pp. 179–187. Springer, Heidelberg (2012)
Elliot, M., Lomax, S., Mackey, E., Purdam, K.: Data Environment Analysis and the Key Variable Mapping System. In: Domingo-Ferrer, J., Magkos, E. (eds.) PSD 2010. LNCS, vol. 6344, pp. 138–147. Springer, Heidelberg (2010), http://www.springerlink.com/index/6KL805434G016U15.pdf (July 13, 2012)
Elliot, M., Dale, A.: Scenarios of attack: the data intruder’s perspective on statistical disclosure risk. Netherlands Official Statistics 14, 6–10 (1999)
Domingo-Ferrer, J., Torra, V.: A critique of k-anonymity and some of its enhancements. In: Third International Conference on Availability, Reliability and Security, ARES 2008, pp. 990–993 (2008), http://ieeexplore.ieee.org/xpls/abs_all.jsp?rnumber=4529451 (accessed July 14, 2012)
Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Nordholt, E., Spicer, K., de Wolf, P.-P.: Statistical Disclosure Control. Wiley Series in Survey Methodology. John Wiley & Sons, London (2012)
Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 10, 557–570 (2001)
Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. IEEE Transactions on Knowledge and Data Engineering 14(1), 189–201 (2002)
Domingo-Ferrer, J., Muralidhar, K., Ruffian-Torrell, G.: Anonymization Methods for Taxonomic Microdata. In: Domingo-Ferrer, J., Tinnirello, I. (eds.) PSD 2012. LNCS, vol. 7556, pp. 90–102. Springer, Heidelberg (2012)
World Health Organization. International Classification of Diseases. Geneva, 9th Revision, Clinical Modification, 6th edn. (2008), http://icd9cm.chrisendres.com/
Dalenius, T., Reiss, S.P.: Data-swapping: A Technique for Disclosure Control. Journal of Statistical Planning and Inference 6, 73–85 (1982)
Muralidhar, K., Sarathy, R.: Data Shuffling-A New Masking Approach for Numerical Data. Management Science 52(5), 658–670 (2006)
Muralidhar, K., Sarathy, R., Dandekar, R.: Why Swap when you can Shuffle? A Comparison of the Proximity Swap and the Data Shuffle for Numeric Data. In: Domingo-Ferrer, J., Franconi, L. (eds.) PSD 2006. LNCS, vol. 4302, pp. 164–176. Springer, Heidelberg (2006)
Raftery, A.E.: Choosing models for cross-classifications. American Sociological Review 51(1), 145–146 (1986)
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McCaa, R., Muralidhar, K., Sarathy, R., Comerford, M., Esteve-Palos, A. (2014). Controlled Shuffling, Statistical Confidentiality and Microdata Utility: A Successful Experiment with a 10% Household Sample of the 2011 Population Census of Ireland for the IPUMS-International Database. In: Domingo-Ferrer, J. (eds) Privacy in Statistical Databases. PSD 2014. Lecture Notes in Computer Science, vol 8744. Springer, Cham. https://doi.org/10.1007/978-3-319-11257-2_25
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DOI: https://doi.org/10.1007/978-3-319-11257-2_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11256-5
Online ISBN: 978-3-319-11257-2
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