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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:


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: 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


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://


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.