A Probabilistic Procedure for Anonymisation, for Assessing the Risk of Re-identification and for the Analysis of Perturbed Data Sets

Arima, S. and S. Polettini. 2019. “A Unit Level Small Area Model with Misclassified Covariates.” Journal of the Royal Statistical Society, Series A. Early View: DOI: https://doi.org/10.1111/rssa.12468.10.1111/rssa.12468Search in Google Scholar

Brand, R. 2002. Microdata Protection Through Noise Addition. In Inference control in statistical databases, 97–116. Berlin, Heidelberg: Springer.10.1007/3-540-47804-3_8Search in Google Scholar

Chambers, R.L. and R. Dunstan. 1986. “Estimating Distribution Functions from Survey Data.” Biometrika 73: 597–604. DOI: https://doi.org/10.2307/2336524.10.2307/2336524Search in Google Scholar

Charest, A.-S. 2010. “How Can we Analyse Differentially-private Synthetic Datasets?” Journal of Privacy and Confidentiality 2: 21–33. DOI: https://doi.org/10.29012/jpc.v2i2.589.10.29012/jpc.v2i2.589Search in Google Scholar

Cox, L., A.F. Karr, and S.K. Kinney. 2011. “Risk-Utility Paradigms for Statistical Disclosure Limitation: How to Think, But Not How to Act.” International Statistical Review 79(2): 160–183. DOI: https://doi.org/10.1111/j.1751-5823.2011.00140.x.10.1111/j.1751-5823.2011.00140.xSearch in Google Scholar

Delaigle, A. and P. Hall. 2008. “Using SIMEX for Smoothing-Parameter Choice in Errors-in-Variables Problems.” Journal of the American Statistical Association 103(481): 280–287.10.1198/016214507000001355Search in Google Scholar

Duncan, G.T. and S. Mukherjee. 2000. “Optimal Disclosure Limitation Strategy in Statistical Databases: Deterring Tracker Attacks Through Additive Noise.” Journal of the American Statistical Association 95(451): 720–729.10.1080/01621459.2000.10474260Search in Google Scholar

Dwork, C. 2006. “Differential Privacy.” In ICALP 2006, edited by M. Bugliesi, B. Preneel, V. Sassone, and I. Wegener, 4052: 1–12. Lecture Notes in Computer Science. Heidelberg: Springer.Search in Google Scholar

Dwork, C. and A. Roth. 2014. “The Algorithmic Foundations of Differential Privacy.” Foundations and Trends in Theoretical Computer Science 9: 211–407. DOI: https://doi.org/10.1561/0400000042.10.1561/0400000042Search in Google Scholar

Elliot, M.J. and A. Dale. 1999. “Scenarios of attach: the data intruder’s perspective on statistical disclosure risk.” Netherlands Official Statistics 14: 6–10. Available at: https://www.researchgate.net/profile/Ton_De_Waal/publication/255565237_Exact_disclosure_in_a_super-table/links/0c960539450114582d000000.pdf#page=6 (accessed February 2020).Search in Google Scholar

Fuller, W.A. 1993. “Masking procedures for microdata disclosure limitation.” Journal of Official Statistics 9: 383–406. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/masking-procedures-for-microdata-disclosure-limitation.pdf (accessed February 2020).Search in Google Scholar

Fuller, W.A. 2006. Measurement error models. Chichester: John Wiley and Sons.Search in Google Scholar

Goldstein, H. 2011. Multilevel Statistical Models. Chichester: Wiley.10.1002/9780470973394Search in Google Scholar

Goldstein, H. and W. Browne. 2016. “Multilevel models: current developments”. Wiley Online Library. Available at: www.wileyonlinelibrary.com/ref/stats.10.1002/9781118445112.stat07853Search in Google Scholar

Goldstein, H., W.J. Browne, and C. Charlton. 2017. “An MCMC procedure for handling measurement and misclassification errors alongside missing data in multilevel multi- variate generalised linear models with an application to a study of Australian youth.” Journal of Applied Statistics. DOI: https://doi.org/10.1080/02664763.2017.1322558.10.1080/02664763.2017.1322558Search in Google Scholar

Goldstein, H., J. Carpenter, and M. Kenward. 2018. “Bayesian models for weighted data with missing values: a bootstrap approach.” Journal of the Royal Statistical Society, Series C. DOI: https://doi.org/10.1111/rssc.12259.10.1111/rssc.12259Search in Google Scholar

Goldstein, H., J. Carpenter, M. Kenward, and K. Levin. 2009. “Multilevel models with multivariate mixed response types.” Statistical Modelling 9(3): 173–197. DOI: https://doi.org/10.1177/1471082X0800900301.10.1177/1471082X0800900301Search in Google Scholar

Goldstein, H., J. Rasbash, M. Yang, G. Woodhouse, H. Pan, D. Nuttall, and S. Thomas. 1993. “A Multilevel Analysis of School Examination Results.” Oxford Review of Education 19(4): 425–433.10.1080/0305498930190401Search in Google Scholar

Gouweleeuw, J., P. Kooiman, L.C.R.J. Willenborg, and P.P. de Wolf. 1998. “Post Randomisation for Statistical Disclosure Control: Theory and Implementation.” Journal of Official Statistics 14: 463–478. Available at: https://pdfs.semanticscholar.org/cd28/1be11657b944b74169b8fe35dddb91d558e8.pdf (accessed February 2020).Search in Google Scholar

Hundepool, A., J. Domingo-Ferrer, L. Francono, S. Giessing, E. Schulte-Nordholt, K. Spicer, and P.P. de Wolf. 2012. Statistical Disclosure Control. Wiley Series in Survey Methodology. Chichester: John Wiley and Sons.10.1002/9781118348239Search in Google Scholar

Hwang, J.T. 1986. “Multiplicative Errors-in-Variables Models with Applications to Recent Data” Released by the U.S. Department of Energy. Journal of the American Statistical Association 81(395): 680–688. DOI: https://doi.org/10.1080/01621459.1986.10478321.10.1080/01621459.1986.10478321Search in Google Scholar

Kim, J.J. 1986. A Method for Limiting Disclosure in Micro-data Based on Random Noise and Transformation. ASA Proceedings of the Section on SRM: 370–374. Available at: http://www.asasrms.org/Proceedings/papers/1986_069.pdf (accessed February 2020).Search in Google Scholar

Little, R.J.A. 1993. “Statistical analysis of masked data.” Journal Official Statistics 9: 407–426. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/statistical-analysis-of-masked-data.pdf (accessed February 2020).Search in Google Scholar

MATLAB. 2017. Available at: https://www.mathworks.com/products/matlab.html (accessed October 2017).Search in Google Scholar

Reiter, J.P. 2005. “Releasing Multiply Imputed, Synthetic Public-Use Microdata: An Illustration and Empirical Study.” Journal of the Royal Statistical Society, Series A 168(1): 185–205. DOI: https://doi.org/10.1111/j.1467-985X.2004.00343.x.10.1111/j.1467-985X.2004.00343.xSearch in Google Scholar

Reiter, J.P. and R. Mitra. 2009. “Estimating Risks of Identification Disclosure in Partially Synthetic Data.” Journal of Privacy and Confidentiality 1(1): 99–110. DOI: https://doi.org/10.29012/jpc.v1i1.567.10.29012/jpc.v1i1.567Search in Google Scholar

Richardson, S. and W.R. Gilks. 1993. “Conditional independence models for epidemiological studies with covariate measurement error.” Statistics in Medicine 12: 1703–1722. DOI: https://doi.org/10.1002/sim.4780121806.10.1002/sim.47801218068248663Search in Google Scholar

Rinott, Y., C. O’Keefe, N. Shlomo, and C. Skinner. 2018. “Confidentiality and Differential Privacy in the Dissemination of Frequency Tables.” Statistical Sciences 33(3): 358–385. DOI: https://doi.org/10.1214/17-STS641.10.1214/17-STS641Search in Google Scholar

Rubin, D.B. 1993. “Discussion Statistical Disclosure Limitation.” Journal of Official Statistics 9: 461–468. Available at: https://www.scb.se/contentassets/ca21efb41-fee47d293bbee5bf7be7fb3/discussion-statistical-disclosure-limitation2.pdf (accessed February 2020).Search in Google Scholar

SdcMicro. 2019. Available at: http://cran.r-project.org/web/packages/sdcMicro/sdcMicro.pdf.Search in Google Scholar

Shlomo, N. 2010. “Measurement Error and Statistical Disclosure Control.” In PSD 2010: Privacy in Statistical Databases, edited by J. Domingo-Ferrer and E. Magkos, 6344: 118–126. Springer LNCS. DOI: https://doi.org/10.1007/978-3-642-15838-4_11.10.1007/978-3-642-15838-4_11Search in Google Scholar

Shlomo, N. and T. de Waal. 2008. “Protection of Micro-data Subject to Edit Constraints Against Statistical Disclosure.” Journal of Official Statistics 24(2): 1–26. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/protection-of-micro-data-subject-to-edit-constraints-against-statistical-disclosure.pdf (accessed February 2020).Search in Google Scholar

Shlomo, N. and C.J. Skinner. 2010. “Assessing the Protection Provided by Misclassification-Based Disclosure Limitation Methods for Survey Microdata.” Annals of Applied Statistics 4(3): 1291–1310. DOI: https://doi.org/10.1214/09-AOAS317.10.1214/09-AOAS317Search in Google Scholar

Tendick, P. 1991. “Optimal Noise Addition for Preserving Confidentiality in Multivariate Data.” Journal of Statistical Planning and Inference 27(3): 341–353. DOI: https://doi.org/10.1016/0378-3758(91)90047-I.10.1016/0378-3758(91)90047-ISearch in Google Scholar

Ting, D., S. Fienberg, and M. Trottini. 2008. “Random orthogonal matrix masking methodology for microdata release.” International Journal on Information and Computer Security 2(1): 86–105. DOI: https://doi.org/10.1504/IJICS.2008.016823.10.1504/IJICS.2008.016823Search in Google Scholar

Willenborg, L. and T. de Waal. 2001. “Elements of Statistical Disclosure Control in Practice.” Lecture Notes in Statistics, 155. New York: Springer-Verlag.10.1007/978-1-4613-0121-9Search in Google Scholar

Winkler, W.E. 1998. “Re-identification Methods for Evaluating the Confidentiality of Analytically Valid Microdata.” Research in Official Statistics 1: 87–104. Available at: https://www.census.gov/srd/papers/pdf/rrs2005-09.pdf.Search in Google Scholar

Woo, Y.M.J. and A. Slavkovic. 2014. “Generalized Linear Models with Variables Subject to Post Randomization Method.” Italian Journal of Applied Statistics 24(1): 29–56. Available at: http://sa-ijas.stat.unipd.it/sites/sa-ijas.stat.unipd.it/files/2%20Woo%20pg%20(1).pdf (accessed February 2020).Search in Google Scholar