응용통계연구 (The Korean Journal of Applied Statistics)

  • 제24권1호
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  • Pages.127-136
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  • 2011
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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승법잡음모형을 이용한 통계적 노출조절기법의 적용

Application of a Statistical Disclosure Control Techniques Based on Multiplicative Noise

  • 김영원 (숙명여자대학교 통계학과) ;
  • 김태연 (숙명여자대학교 통계학과) ;
  • 김계남 (숙명여자대학교 통계학과)
  • Kim, Young-Won (Department of Statistics, Sookmyung Women's University) ;
  • Kim, Tae-Yeon (Department of Statistics, Sookmyung Women's University) ;
  • Ki, Kye-Nam (Department of Statistics, Sookmyung Women's University)
  • 투고 : 20101200
  • 심사 : 20110100
  • 발행 : 2011.02.28
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초록

본 연구에서는 통계기관에서 마이크로자료를 제공할 때, 연속형 변수를 마스킹하는 기법으로 잘 알려진 승법잡음모형을 적용하는 경우 원자료의 평균과 분산을 유지할 수 있는 변수 변환 방안을 제시하고, 제시된 방법의 적절성과 다양한 잡음생성 분포에 따른 마스킹자료의 유용성을 검토하였다. 아울러 여러 변수들을 대상으로 승법잡음모형을 적용하는 경우 변수들 간의 상관관계를 유지하기 위해서는 잡음생성과정에서 어떤 측면이 고려되어야 하는지 살펴보았다. 본 연구에서는 제시된 변수 변환 방법의 적절성과 자료의 유용성 등을 평가하기 위해 우리나라 가계조사자료를 이용한 모의실험을 수행하였다.

Multiplicative noise model is the one of popular method for masking continuous variables. In this paper, we propose the transformation on the variable to which random noise was multiplied. An advantage of the masking method using proposed transformation is that the masking data users can obtain the unbiased values of mean and variance of original (unmasked) data. We also consider the data utility and correlation structure of variables when we apply the proposed multiplicative noise scheme. To investigate the properties of the method of masking based on multiplicative noise, a simulation study has been conducted using the 2008 Householder Income and Expenditure Survey data.

키워드

참고문헌

  1. 김규성 (2009). 마이크로데이터 제공과 통계적 노출조절기법, <한국통계학회논문집>, 16, 1-11. https://doi.org/10.5351/CKSS.2009.16.1.001
  2. 정동명, 김종익, 강동환 (2007). 인구센서스자료의 비밀보호방법, <응용통계연구>, 12, 95-120.
  3. 정동명, 김종익, 김경미 (2009). 잡음을 이용한 가계조사자료의 정보노출제한방법, <응용통계연구>, 22, 141-151. https://doi.org/10.5351/KJAS.2009.22.1.141
  4. 정동명, 정미옥 (2008). 인구주택총조사 마이크로자료의 개인정보 노출제한방법, <응용통계연구>, 21, 313-325. https://doi.org/10.5351/KJAS.2008.21.2.313
  5. 통계청 (2008). 가계조사 조사지침서.
  6. Dalenius, T. and Reiss, S. P. (1982). Data swapping: A technique for disclosure control, Journal of Statistical Planning and Inference, 6, 73-85. https://doi.org/10.1016/0378-3758(82)90058-1
  7. Fuller, W. A. (1993). Masking procedures for microdata disclosure limitation, Journal of Official Statisitcs, 9, 383-406.
  8. Karr, A. F., Kohnen, C. N., Oganian, A., Reiter, J. P. and Sanil, A. P. (2006). A framework for evaluating the utility of data altered to protect confidentiality, The American Statistician, 60, 1-9. https://doi.org/10.1198/000313006X93258
  9. Kim, J. (1986). A method for limiting disclosure in microdata based on random noise and transformation, American Statistical Association Proceedings of the Section on Survey Research Methods, 303-308.
  10. Kim, J. (2007). Application of the truncated triangular and trapezoidal distributions for developing multi-plicative noise, American Statistical Association Proceedings of the Section on Survey Research Methods, 2723-2729.
  11. Kim, J. and Winkler, W. E. (2001). Multiplicative noise for masking continuous data, American Statistical Association Proceedings of the Section on Survey Research Methods, CD-ROM.
  12. Torra, V. (2004). Microaggregation for categorical variavbles: A median based approach, In Domingo-Ferrer, J. and Torra, V. Editors, Privacy in Statistical Databases, Lecture Notes in Computer Science, 3050, 162-174. https://doi.org/10.1007/978-3-540-25955-8_13
  13. Woo, M. J., Reiter, P., Anna, O. and Karr, A. F. (2009). Global measures of data utility for microdata masked for dislosure limitation, The Journal of Privacy and Confidentiality, 1, 111-124.

피인용 문헌

  1. Study on a Measurement of Disclosure Risk of Microdata by Similarity vol.25, pp.5, 2012, https://doi.org/10.5351/KJAS.2012.25.5.743
  2. Estimating nonlinear regression with and without change-points by the LAD method vol.63, pp.4, 2011, https://doi.org/10.1007/s10463-009-0256-y
  3. Penalized least absolute deviations estimation for nonlinear model with change-points vol.52, pp.2, 2011, https://doi.org/10.1007/s00362-009-0236-6
  4. Empirical likelihood for nonlinear models with missing responses vol.83, pp.4, 2013, https://doi.org/10.1080/00949655.2011.635305
  5. Statistical disclosure control for public microdata: present and future vol.29, pp.6, 2016, https://doi.org/10.5351/KJAS.2016.29.6.1041
  6. Least absolute value regression: recent contributions vol.75, pp.4, 2005, https://doi.org/10.1080/0094965042000223680