Split leverage: attacking the confidentiality of linked databases by partitioning

Rai, Tapan ; Hall, Joanne L.

ANZIAM Journal, Tome 55 (2014), / Harvested from Australian Mathematical Society

Résumé

This article considers the risk of disclosure in linked databases when statistical analysis of micro-data is permitted. The risk of disclosure needs to be balanced against the utility of the linked data. The current work specifically considers the disclosure risks in permitting regression analysis to be performed on linked data. A new attack based on partitioning of the database is presented. References Anton, H., Rorres, C. Elementary Linear Algebra, 10th edition. John Wiley and Sons Inc, Hoboken, NJ, 2010. Chipperfield, J. O., Yu, F., Gare, M. Providing access to microdata for statistical purposes: Experiences of the Australian Bureau of Statistics with remote analysis servers. Symposium 2011, Catalogue no. 11-522-XCB, Statistics Canada, pp.187–194, http://publications.gc.ca/collections/collection_2013/statcan/11-522-x/CS11-522-2011-eng.pdf, 2011. Cox, L. Confidentiality Issues For Statistical Database Query Systems. Invited Paper for Joint UNECE/Eurostat Seminar on Integrated Statistical Information Systems and Related Matters, Geneva Switzerland, http://www.unece.org/stats/documents/ces/sem, 2002. Dwork, C., McSherry, F., Nissim, K., Smith, A. Calibrating noise to sensitivity in private data analysis. Proceedings of the 3rd Theory of Cryptography Conference, LNCS 3876, pp. 265–284, 2006. doi:10.1007/11681878_14 Duncan, G. T., Elliott, M., Salazar-Gonzalez, J.-J. Statistical Confidentiality: Principles and Practice. Springer, NY, 2012. doi:10.1007/978-1-4419-7802-8 Gomatam, S., Karr, A., Reiter, J., Sanil, A. Data dissemination and disclosure limitation in a world without microdata: A risk-utility framework for remote access systems. Statistical Science 20(2), pp. 163–177, 2005. doi:10.1214/088342305000000043 Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Norholdt, E., Spicer, K., de Wolf, P.-P. Statistical Disclosure Control. Wiley, UK, 2012. O'Keefe, C., Chipperfield, J. A Summary of Attack Methods and Confidentiality Protection Measures for Fully Automated Remote Analysis Systems. International Statistical Review 81(3), pp. 426–455, 2013. doi:10.1111/insr.12021 O'Keefe, C., Good, N. Regression output from a remote analysis server. Data and Knowledge Engineering 68(11), pp. 1175–1186, 2009. doi:10.1016/j.datak.2009.06.009 Reiter, J. P. Model diagnostics for remote-access regression servers. Statistics and Computing 13(4), pp. 371–380, 2003. doi:10.1023/A:1025623108012 Reiter, J. P., Kohnen, C. N. Categorical data regression diagnostics for remote servers. Journal of Statistical Computation and Simulation 75(11), pp. 889–903, 2005. doi:10.1080/00949650412331299184 Reznek, A. P. Recent confidentiality research related to access to enterprise microdata. Comparative Analysis of Enterprise Microdata Conference Chicago, IL, USA, http://www.oecd.org/std/37503027.pdf, 2006. Ritchie, F. Disclosure Controls for Regression Outputs. London: Mimeo, Office of National Statistics, London, http://www.wiserd.ac.uk/files/7913/6543/6668/WISERD_WDR_006.pdf, 2006. Sparks, R., Carter, C., Donnelly, J., Duncan, J., O'Keefe, C., Ryan, L. A framework for performing statistical analyses of unit record health data without violating either privacy or confidentiality of individuals. Proceedings of the 55th Session of the International Statistical Institute, Sydney, 2005. Sparks, R., Carter, C., Donnelly, J. B., O'Keefe, C., Duncan, J., Keighley, T., McAullay, D. Remote access methods for exploratory data analysis and statistical modelling: Privacy-Preserving Analytics. Computer Methods and Programs in Biomedicine 91(3), pp. 208–222, 2008. doi:10.1016/j.cmpb.2008.04.001 Wetherill, G. B. Regression Analysis with Applications. Chapman and Hall Ltd, London, 1986. Wolfram Research, Inc. Mathematica Version 8.0. Wolfram Research, Inc., Champaign, IL, USA, 2010.

Détail

Publié le : 2014-01-01
DOI : https://doi.org/10.21914/anziamj.v55i0.8920