Statistical Methods of Confidentiality for Micro Data and Developing an Object Oriented Statistical Disclosure Control Software
Abstract
Abstract: Statistical offices collect large
amounts of data for statistical purposes. A basic principle in statistical
legal frameworks is that data collected for statistical purposes may only be
used for the production of statistics. However, statistical offices experience
increasing pressure from scientists and governments to provide access to
detailed data. There are high costs and risks associated with micro data
access. When micro data sets are released, it is possible that external users
may attempt to breach confidentiality. In this paper, an object-oriented
statistical disclosure control software, OOSDCS was developed to facilitate statisticians
and to apply statistical disclosure control methods to create safe micro data
files. The developed hybrid, flexible and interactive software was successfully
applied as a disclosure control method.
References
- [1] Quinto, W. and Singer, S. “Trade secrets : law and practice”, Oxford University, Press New York , 2009. [2] Elliot, M.J. and Dale, A.,”Scenarios of attack : the data intruder's perspective on statistical disclosure risk”, Netherlands Official Statistical, Spring,1999, pp. 6-10. [3] ASA, “Data Access and Personal Privacy: Appropriate Methods of Disclosure Control”, American Statistical Association Notice, 2008. [4] Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Lenz R., Longhurst, J.,. Schulte Nordholt, E., Seri, G., and de Wolf, P.P., “Handbook on Statistical Disclosure Control”, 2009, vol 1.1, ESSnet SDC. [5] Gouweleeuw, J., Kooiman, M., Willenborg, P., and Wolf, de P. P., “Post randomization for statistical disclosure control: Theory and implementation”, Journal of Official Statistics, issue 14, 1998a, pp. 463-478. [6] Gouweleeuw, J.M., P. Kooiman, L.C.R.J. Willenborg and P.P. de Wolf (1998b), The post randomisation method for protecting micropdata”, Qüestiió, Quaderns d'Estadística i nvestigació Operativa, vol. 22 issue 1, 1998b, pp. 145 – 156. [7] Warner, S.L,“Randomized Response; a survey technique for eliminating evasive answer bias”, Journal of the American Statistical Association, vol. 57, 1965, pp. 622 - 627. [8] Hundepool, A., de Wetering, A.V., Ramaswamy, R., Franconi, L., Capobianchi, A., De Wolf, P.P., Domingo J. F., Torra, V., Brand, R., and Giessing, S., “µ-ARGUS version 4.2 Software and User's Manual”, Statistics Netherlands, Voorburg NL, 2008. [9] Templ, M., “Statistical Disclosure Control for Microdata Using the R-Package sdcMicro”, Transactions on Data Privacy, vol.1-2, 2008, pp. 67 – 85. [10] Manning, A. M., and Haglin, D. J., “A new algorithm for finding minimal sample uniques for use in statistical disclosure assessment”, IEEE International Conference on Data Mining (ICDM05), Nov. 2005, pp 290-297. [11] Templ, M., “sdcMicro: Statistical Disclosure Control methods for the generation of public- and scientific-use files”, 2009. http://cran.r-project.org/web/packages/sdcMicro [12] Capobianchi, A., Polettini, S., and Lucarelli, M., “Strategy for the implementation of individual risk methodology into µ-ARGUS”, Technical report, Report for the CASC project. No: 1.2-D1, 2001. [13] de Wolf , P.P., Hundepool, A., Giessing, S., Salazar , J.J., Castro, J., “µ-argus version 4.1 software and user’s manual”, Argus Open Source-project, Statistics Netherland, P.O. Box 24500, 2014.
Statistical Methods of Confidentiality for Micro Data and Developing an Object Oriented Statistical Disclosure Control Software
Abstract
Özet: İstatistik ofisleri, istatistiksel amaçlar
için büyük miktarda veri toplamaktadır. İstatistiki yasal çerçevelerdeki temel
ilke istatistiksel amaçlar için toplanan verilerin yalnızca istatistik üretimi
için kullanılabilmesidir. Bununla birlikte, istatiksel bürolar, bilim
insanlarının ve hükümetlerin detaylı verilere erişim sağlamak için gittikçe
artan baskıyı yaşıyor. Mikro veri erişimi ile ilişkili yüksek maliyetler ve
riskler vardır. Mikro veri setleri serbest bırakıldığında, harici
kullanıcıların gizliliği ihlal etmeye çalışması olasıdır. Bu yazıda,
istatistikçileri kolaylaştırmak ve güvenli mikro veri dosyaları oluşturmak için
istatistiksel açıklama kontrol yöntemleri uygulamak için nesne yönelimli
istatistiksel açıklama kontrol yazılımı OOSDCS geliştirilmiştir. Geliştirilen
hibrid, esnek ve interaktif yazılım, bir açıklama kontrol yöntemi olarak
başarıyla uygulanmıştır.
References
- [1] Quinto, W. and Singer, S. “Trade secrets : law and practice”, Oxford University, Press New York , 2009. [2] Elliot, M.J. and Dale, A.,”Scenarios of attack : the data intruder's perspective on statistical disclosure risk”, Netherlands Official Statistical, Spring,1999, pp. 6-10. [3] ASA, “Data Access and Personal Privacy: Appropriate Methods of Disclosure Control”, American Statistical Association Notice, 2008. [4] Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Lenz R., Longhurst, J.,. Schulte Nordholt, E., Seri, G., and de Wolf, P.P., “Handbook on Statistical Disclosure Control”, 2009, vol 1.1, ESSnet SDC. [5] Gouweleeuw, J., Kooiman, M., Willenborg, P., and Wolf, de P. P., “Post randomization for statistical disclosure control: Theory and implementation”, Journal of Official Statistics, issue 14, 1998a, pp. 463-478. [6] Gouweleeuw, J.M., P. Kooiman, L.C.R.J. Willenborg and P.P. de Wolf (1998b), The post randomisation method for protecting micropdata”, Qüestiió, Quaderns d'Estadística i nvestigació Operativa, vol. 22 issue 1, 1998b, pp. 145 – 156. [7] Warner, S.L,“Randomized Response; a survey technique for eliminating evasive answer bias”, Journal of the American Statistical Association, vol. 57, 1965, pp. 622 - 627. [8] Hundepool, A., de Wetering, A.V., Ramaswamy, R., Franconi, L., Capobianchi, A., De Wolf, P.P., Domingo J. F., Torra, V., Brand, R., and Giessing, S., “µ-ARGUS version 4.2 Software and User's Manual”, Statistics Netherlands, Voorburg NL, 2008. [9] Templ, M., “Statistical Disclosure Control for Microdata Using the R-Package sdcMicro”, Transactions on Data Privacy, vol.1-2, 2008, pp. 67 – 85. [10] Manning, A. M., and Haglin, D. J., “A new algorithm for finding minimal sample uniques for use in statistical disclosure assessment”, IEEE International Conference on Data Mining (ICDM05), Nov. 2005, pp 290-297. [11] Templ, M., “sdcMicro: Statistical Disclosure Control methods for the generation of public- and scientific-use files”, 2009. http://cran.r-project.org/web/packages/sdcMicro [12] Capobianchi, A., Polettini, S., and Lucarelli, M., “Strategy for the implementation of individual risk methodology into µ-ARGUS”, Technical report, Report for the CASC project. No: 1.2-D1, 2001. [13] de Wolf , P.P., Hundepool, A., Giessing, S., Salazar , J.J., Castro, J., “µ-argus version 4.1 software and user’s manual”, Argus Open Source-project, Statistics Netherland, P.O. Box 24500, 2014.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Makaleler |
| Authors | |
| Publication Date | September 30, 2017 |
| Submission Date | May 19, 2017 |
| Published in Issue | Year 2017 |
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