TY - JOUR T1 - Statistical Methods of Confidentiality for Micro Data and Developing an Object Oriented Statistical Disclosure Control Software TT - Statistical Methods of Confidentiality for Micro Data and Developing an Object Oriented Statistical Disclosure Control Software AU - Haşıloğlu, Abdulsamet AU - Balı, Abdulkadir PY - 2017 DA - September DO - 10.31202/ecjse.315033 JF - El-Cezeri JO - El-Cezeri Journal of Science and Engineering PB - Tayfun UYGUNOĞLU WT - DergiPark SN - 2148-3736 SP - 319 EP - 333 VL - 4 IS - 3 LA - en AB - Abstract: Statistical offices collect largeamounts of data for statistical purposes. A basic principle in statisticallegal frameworks is that data collected for statistical purposes may only beused for the production of statistics. However, statistical offices experienceincreasing pressure from scientists and governments to provide access todetailed data. There are high costs and risks associated with micro dataaccess. When micro data sets are released, it is possible that external usersmay attempt to breach confidentiality. In this paper, an object-orientedstatistical disclosure control software, OOSDCS was developed to facilitate statisticiansand to apply statistical disclosure control methods to create safe micro datafiles. The developed hybrid, flexible and interactive software was successfullyapplied as a disclosure control method. KW - Statistical Confidentiality KW - Statistical Disclosure Control KW - Disclosure Risk KW - Micro Data N2 - Özet: İstatistik ofisleri, istatistiksel amaçlariçin büyük miktarda veri toplamaktadır. İstatistiki yasal çerçevelerdeki temelilke istatistiksel amaçlar için toplanan verilerin yalnızca istatistik üretimiiçin kullanılabilmesidir. Bununla birlikte, istatiksel bürolar, biliminsanlarının ve hükümetlerin detaylı verilere erişim sağlamak için gittikçeartan baskıyı yaşıyor. Mikro veri erişimi ile ilişkili yüksek maliyetler veriskler vardır. Mikro veri setleri serbest bırakıldığında, haricikullanıcıların gizliliği ihlal etmeye çalışması olasıdır. Bu yazıda,istatistikçileri kolaylaştırmak ve güvenli mikro veri dosyaları oluşturmak içinistatistiksel açıklama kontrol yöntemleri uygulamak için nesne yönelimliistatistiksel açıklama kontrol yazılımı OOSDCS geliştirilmiştir. Geliştirilenhibrid, esnek ve interaktif yazılım, bir açıklama kontrol yöntemi olarakbaşarıyla uygulanmıştır. CR - [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. UR - https://doi.org/10.31202/ecjse.315033 L1 - https://dergipark.org.tr/en/download/article-file/346099 ER -