Comments on four papers on synthetic data in Volume 32 Issue 1 the Statistical Journal of the IAOS

Rubin D.B. , Discussion of statistical disclosure limitation, Journal of Official Statistics 9(2) (1993), 461-468.

Little R.J. , Statistical analysis of masked data, Journal of Official Statistics 9(2) (1993), 407-426.

Fienberg S.E , A radical proposal for the provision of micro-data samples and the preservation of confidentiality, Technical report, Department of Statistics, Carnegie-Mellon University. (1994).

Reiter J.P. , Satisfying disclosure restrictions with synthetic data sets, Journal of Official Statistics 18(4) (2002), 1-19.

Raghunathan T.E. , Reiter J.P. and Rubin D.B. , Multiple imputation for statistical disclosure limitation, Journal of Official Statistics 19(1) (2003), 1-16.

Drechsler J. , Synthetic Datasets for Statistical Disclosure Control Theory and Implementation, New York: Springer, 2011.

Abowd J.M. , Stinson M. and Benedetto G. , Final Report to the Social Security Administration on the SIPP/SSA/IRS Public Use File Project. U.S. Census Bureau; (2006). Available from: http://www2.vrdc.cornell.edu/news/?p=308.

Kinney S.K. , Reiter J.P. , Reznek A.P. , Miranda J. , Jarmin R.S. and Abowd J.M. , Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database, International Statistical Review 79(3) (2011), 362-384. Available from: http://ideas.repec.org/a/bla/istatr/v79y2011i3 p362-384.html.

Drechsler J. and Vilhuber L. , A first step towards a German SynLBD: Consructing a German Longitudinal Business Database. Statistical Journal of the IAOS 30(2) (2014), 137-142.

Miranda J. and Vilhuber L. , Using partially synthetic micr-\linebreak odata to protect sensitive cells in business statistics, Statistical Journal of the IAOS 32(1) (2016), 69-80.

Wei L. and Reiter J.P. , Releasing synthetic magnitude microdata constrained to fixed marginal totals, Statistical Journal of the IAOS 32(1) (2016), 93-108.

MacLure D. and Reiter J.P. , Assessing disclosure risks for synthetic data with arbitrary intruder knowledge, Statistical Journal of the IAOS 32(1) (2016), 109-126.

Schmutte I.M. , Differentially private publication of data on wages and job mobility, Statistical Journal of the IAOS 32(1) (2016), 81-92.

Vilhuber L. , Abowd J.M. and Reiter J.P. , synthetic establishment data around the world, Statistical Journal of the IAOS 32(1) (2016), 65-68.

Nowok B. , Raab G.M. and Dibben C. , synthpop: Bespoke creation of synthetic data in R, Journal of Statistical Software. Forthcoming. (2015). Available from https://cran.r-project.org/web/packages/synthpop/vignettes/synthpop.pdf.

Nowok B. , Raab G.M. and Dibben C. , Assisted methods for providing bespoke synthetic data for the UK longitudinal studies and other sensitive data, Statistical Journal of the IAOS. Submitted (2016).

Raab G.M. , Nowok B. and Dibben C. , Practical synthesis for large samples. Submitted (2016). Available from http:// arxiv.org/abs/1409.0217.

Kinney S.K. , Reiter J.P. and Miranda J. , SynLBD 2.0: Improving the synthetic Longitudinal Business Database, Statistical Journal of the IAOS 30(2) (2014), 129-135.

McLachlan G. and Peel D. , Finite Mixture Models, Wiley, New York, 2000.

Abowd J.M. and Vilhuber L. , How protective are synthetic data? in: Privacy in Statistical Databases, Domingo-Ferrer J. and Saygun Y. , eds, New York: Springer-Verlag, 2008, pp. 239-246.

Charest A.S. , How can we analyze differentially-private synthetic datasets, Journal of Privacy and Confidentiality 2(2) (2010).

McClure D. and Reiter J.P. , Differential privacy and statistical disclosure risk measures: an investigation with binary synthetic data, Transactions on Data Privacy 5(3) (2012), 535-552.