Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography

Spatial Statistics

Volumn 14, Issue , 2015, Pages 439-451

  Quick, Harrison ^a   Holan, Scott H ^b   Wikle, Christopher K ^b   Reiter, Jerome P ^c  

^a CENTERS FOR DISEASE CONTROL AND PREVENTION   (United States)

^b UNIVERSITY OF MISSOURI   (United States)

^c Duke University   (United States)

Author keywords

Dimension reduction; Disclosure; Marked point processes; Multiple imputation; Predictive process; Privacy

Indexed keywords

EID: 84948948141     PISSN: 22116753     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.spasta.2015.07.008     Document Type: Article

References (46)

1. Abowd J.M., Woodcock S.D. Multiply-imputing confidential characteristics and file links in longitudinal linked data. Privacy in Statistical Databases 2004, 290-297. Springer-Verlag, New York. J. Domingo-Ferrer, V. Torra (Eds.).
2. An D., Little R. Multiple imputation: an alternative to top coding for statistical disclosure control. J. R. Stat. Soc. Ser. A 2007, 170:923-940.
3. Armstrong M.P., Rushton G., Zimmerman D.L. Geographically masking health data to preserve confidentiality. Stat. Med. 1999, 18:495-525.
4. Banerjee S., Finley A.O., Waldmann P., Ericsson T. Hierarchical spatial process models for multiple traits in large genetic trials. J. Amer. Statist. Assoc. 2010, 105:506-521.
5. Banerjee S., Gelfand A.E., Finley A.O., Sang H. Gaussian predictive process models for large spatial data sets. J. R. Stat. Soc. Ser. B Stat. Methodol. 2008, 70:825-848.
6. Bartlett M.S. The spectral analysis of two-dimensional point process. Biometrika 1964, 51:299-311.
7. Bradley J.R., Cressie N., Shi T. Comparing and selecting spatial predictors using local criteria (with discussion). TEST 2015, 24:1-28.
8. Burgette L.F., Reiter J.P. Multiple-shrinkage multinomial probit models with applications to simulating geographies in public use data. Bayesian Anal. 2013, 8:1-26.
9. Cressie N., Wikle C.K. Statistics for Spatio-Temporal Data 2011, Wiley, Hoboken, NJ.
10. Dalenius T., Reiss S.P. Data-swapping: A technique for disclosure control. J. Statist. Plann. Inference 1982, 6:73-85.
11. Diggle P.J. Statistical Analysis of Spatial and Spatio-Temporal Point Patterns 2013, Chapmand & Hall/CRC Press, Boca Raton.
12. Drechsler J., Reiter J.P. Sampling with synthesis: A new approach for releasing public use census microdata. J. Amer. Statist. Assoc. 2010, 105:1347-1357.
13. Finley A.O., Sang H., Banerjee S., Gelfand A.E. Improving the performance of predictive process modeling for large datasets. Comput. Statist. Data Anal. 2009, 8:2873-2884.
14. Fuller W.A. Masking procedures for microdata disclosure limitation. J. Off. Stat. 1993, 9:383-406.
15. Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Anal. 2006, 1:515-533.
16. Hawala S. Producing partially synthetic data to avoid disclosure. Proceedings of the Joint Statistical Meetings 2008, American Statistical Association, Alexandria, VA.
17. Holan S.H., Toth D., Ferreira M.A.R., Karr A. Bayesian multiscale multiple imputation with implications for data confidentiality. J. Amer. Statist. Assoc. 2010, 105:564-577.
18. Hundepool A., Domingo-Ferrer J., Franconi L., Giessing S., Nordholdt E.S., Spicer K., de Wol P.P. Statistical Disclosure Control 2012, Wiley, Chichester.
19. Isham V. Spatial point process models. Handbook of Spatial Statistics 2010, 283-298. Chapman and Hall/CRC. A.E. Gelfand, P.J. Diggle, M. Fuentes, P. Guttorp (Eds.).
20. Kennickell A.B. Multiple imputation and disclosure protection: The case of the 1995 survey of consumer finances. Record Linkage Techniques, 1997 1997, 248-267. National Academy Press, Washington, DC. W. Alvey, B. Jamerson (Eds.).
21. Kinney S.K., Reiter J.P., Reznek A.P., Miranda J., Jarmin R.S., Abowd J.M. Towards unrestricted public use business microdata: The synthetic longitudinal business database. Internat. Statist. Rev. 2011, 79:363-384.
22. Liang S., Carlin B.P., Gelfand A.E. Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information. Ann. Appl. Stat. 2009, 3:943-962.
23. Little R.J.A. Statistical analysis of masked data. J. Off. Stat. 1993, 9:407-426.
24. Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., Vilhuber, L., 2008. Privacy: Theory meets practice on the map. In: IEEE 24th International Conference on Data Engineering, pp. 277-286.
25. Møller J., Syversveen A.R., Waagepetersen R.P. Log Gaussian Cox processes. Scand. J. Statist. 1998, 25(3):451-482.
26. Paiva T., Chakroborty A., Reiter J., Gelfand A. Imputation of confidential data sets with spatial locations using disease mapping models. Stat. Med. 2014, 33:1928-1945.
27. Raghunathan T.E., Reiter J.P., Rubin D.B. Multiple imputation for statistical disclosure limitation. J. Off. Stat. 2003, 19:1-16.
28. Rathbun S.L., Cressie N. A space-time survival point process for a longleaf pine forest in southern Georgia. J. Amer. Statist. Assoc. 1994, 89:1164-1174.
29. Reiter J.P. Satisfying disclosure restrictions with synthetic data sets. J. Off. Stat. 2002, 18:531-544.
30. Reiter J.P. Inference for partially synthetic, public use microdata sets. Surv. Methodol. 2003, 29:181-188.
31. Reiter J.P. Simultaneous use of multiple imputation for missing data and disclosure limitation. Surv. Methodol. 2004, 30:235-242.
32. Reiter J.P. Releasing multiply-imputed, synthetic public use microdata: An illustration and empirical study. J. R. Stat. Soc. Ser. A 2005, 168:185-205.
33. Reiter J.P. Significance tests for multi-component estimands from multiply-imputed, synthetic microdata. J. Statist. Plann. Inference 2005, 131:365-377.
34. Reiter J.P. Using CART to generate partially synthetic, public use microdata. J. Off. Stat. 2005, 21:441-462.
35. Reiter J.P., Drechsler J. Releasing multiply-imputed, synthetic data generated in two stages to protect confidentiality. Statist. Sinica 2010, 20:405-422.
36. Reiter J.P., Raghunathan T.E. The multiple adaptations of multiple imputation. J. Amer. Statist. Assoc. 2007, 102:1462-1471.
37. Ripley B.D. The second-order analysis of stationary point processes. J. Appl. Probab. 1976, 13:255-266.
38. Rubin D.B. Statistical disclosure limitation. J. Off. Stat. 1993, 9:461-468.
39. Si Y., Reiter J.P. A comparison of posterior simulation and inference by combining rules for multiple imputation. J. Stat. Theory Pract. 2011, 5:335-347.
40. Taddy M.A., Kottas A. Mixture modeling for marked point processes. Bayesian Anal. 2012, 7:335-362.
41. Toth D. Data smearing: an approach to disclosure limitation for tabular data. J. Off. Stat. 2014, 30:839-857.
42. VanWey L.K., Rindfuss R.R., Guttman M.P., Entwisle B., Balk D.L. Confidentiality and spatially explicit data: concerns and challenges. Proc. Natl. Acad. Sci. 2005, 102:15337-15342.
43. Wang H., Reiter J.P. Multiple imputation for sharing precise geographies in public use data. Ann. Appl. Stat. 2012, 6:229-252.
44. Willenborg L., de Waal T. Elements of Statistical Disclosure Control 2001, Springer-Verlag, New York.
45. Winkler W.E. Examples of easy-to-implement, widely used methods of masking for which analytic properties are not justified. US Census Bureau Research Report Series, No. 2007-21 2007, Tech. Rep.
46. Zhou Y., Dominici F., Louis T.A. A smoothing approach for masking spatial data. Ann. Appl. Stat. 2010, 4:1451-1475.