JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Study on a Measurement of Disclosure Risk of Microdata by Similarity
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Study on a Measurement of Disclosure Risk of Microdata by Similarity
Cho, Hyeon-Kwan; Kwon, Dae-Hong; Lee, Suk-Hoon;
  PDF(new window)
 Abstract
Researchers using various of statistical data want to obtain microdata for a detailed analysis. Institutes need to provide microdata after masking processes for sensitive data. Many researchers have used the proportion of unique identity for the measurement of disclosure risk. We proposed a new measurement of disclosure risk that considers the case that all identities are the same or similar. As an application example, we compare the newly proposed measurement and the existing measurement using 10667 data in 'Korea Household Income and Expenditure Survey data for 2010'.
 Keywords
Measurement of disclosure risk;microdata;similarity;
 Language
Korean
 Cited by
 References
1.
Bethlehem, J., Keller, W. and Pannekoek, J. (1990). Disclosure control of microdata, Journal of the American Statistical Association, 85, 38-45. crossref(new window)

2.
Chen, G. and Keller-McNulty, S. (1998). Estimation of identification disclosure risk in microdata, Journal of Official Statistics, 14, 79-85.

3.
Duncan, G., Keller-McNulty, S. and Stokes, S. (2001). Disclosure risk vs data utility: The R-U confidentiality map, Technical Report Number 121 December 2001, National Institute of Statistical Sciences.

4.
Eurostat (1996). Manual on Disclosure Control Methods. Luxembourg, Office for Official Publications of the European Communities, Technical Report Number 153 June 2006, National Institute of Statistical Sciences.

5.
FCSM(Federal Committee on Statistical Methodology) (2005). Statistical Policy Working Paper 22(second version).

6.
Fienberg, S. E. and Makov, U. E. (1998). Confidentiality, uniqueness, and disclosure limitation for categorical data, Journal of Official Statistics, 14, 385-397.

7.
Gomatam, S., Karr, A. and Sanil, A. (2003). A Risk Utility Framework for Categorical Data Swapping, Technical Report Number 132 February 2003, National Institute of Statistical Sciences.

8.
Huda, M. N., Yamada, S. and Sonehara, N. (2010). On Identity Disclosure Risk Measurement for Shared Microdata, World Academy of Science, Engineering and Technology, 70, 310-317.

9.
Hwang, H. S. (2012). The Study on the Extended Measurement of Disclosure Risk of Microdata, Master Dissertation, Chungnam National University.

10.
Jeong, D. M., Kim, J. J. and Kim, K. M. (2009). A method of masking based on multiplicative noise, The Korean Journal of Applied Statistics, 22, 141-151. crossref(new window)

11.
Kim, K. Y. (2006). A Study on the Statistical Confidentiality Methodology and Variance Estimation for Census Survey Data, Doctoral Dissertation, Chungnam National University.

12.
Kim, K. Y., Kwon, D. H., Shin, J. E. and Lee, S. H. (2011a). Introduction to Statistical Methods for Confidentiality, FreeAcademy.

13.
Kim, Y.-W., Kim, T.-Y. and Kim, K.-N. (2011b). Application of a statistical disclosure control techniques based on multiplicative noise, The Korean Journal of Applied Statistics, 24, 127-136. crossref(new window)

14.
Kwon, D. H. (2009). A Study on Disclosure Control Method for Disclosure Risk and Utility of Microdata, Doctoral Dissertation, Chungnam National University.

15.
Machanavajjhala, A., Gehrke, J. and Kifer, D. (2006). -diversity: Privacy beyond-anonymity, In Proceedings of the International Conference on Data Engineering Atlanta Engineering, Atlanta.

16.
Samarati, P. (2001). Protecting respondents' identities in microdata release, IEEE Transactions on Knowledge and Data Engineering, 13, 1011-1027.

17.
Shlomo, N. (2010). Releasing Microdata: Disclosure risk estimation, data masking and assessing utility, Journal of Privacy Confidentiality, 2, 73-91.

18.
Skinner, C. J. and Elliot, M. J. (2002). A measure of disclosure risk for microdata, Journal of Royal Statistical Society, B, 855-867.

19.
Takemura, A. (1997). Some Superpopulation Models for Estimating the Number of Population Uniques, University of Tokyo, September 1997.

20.
Xiao, X., Tao, Y. and Koudas, N. (2010). Transparent anonymization: Thwarting adversaries who know the algorithm, ACM Transactions on Database System, 35, April 2010.

21.
Zayatz, L. (1991). Estimation of the number of unique population elements using a sample, In Proceedings of the Section on Survey Research Methods, American Statistical Association, Alexandria, VA, 369-373.