J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the Section on Survey Research Methods, pages 303--308, Alexandria VA, 1986. American Statistical Association. 10 Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Computational Disclosure Control - A Primer on Data Privacy.. - Sweeney (2001) (5 citations) (Correct)
....other data holders, statistics offices are also facing tremendous demand for entity specific data for applications such as data mining, cost analysis, fraud detection and retrospective research. But many of the established statistical database techniques, which involve various ways of adding noise [24] to the data while still maintaining some statistical invariant [25, 26] often destroy the integrity of tuples and so, for many new uses of data, these established techniques are not appropriate. I will further discuss disclosure limitation techniques commonly employed to protect the ....
J. Kim. A method for limiting disclosure of microdata based on random noise and transformation Proceedings of the Section on Survey Research Methods of the American Statistical Association, 370-374. 1986.
Protection Models for Anonymous Databases - Sweeney (Correct)
....11, 12] Like other data holders, statistics offices are also facing tremendous demand for entityspecific data for applications such as data mining, cost analysis, and retrospective research. But many of the established statistical database techniques, which involve various ways of adding noise [13] to the data while still maintaining some statistical invariant [14] often destroy the integrity of tuples [15] Even in cases of tabular releases, statistical offices are finding their established practices failing given the increase of entity specific data and the proliferation of computing ....
J. Kim. A method for limiting disclosure of microdata based on random noise and transformation Proceedings of the Section on Survey Research Methods of the American Statistical Association, 370-374. 1986.
The State of Record Linkage and Current Research Problems - Winkler (1999) (21 citations) (Correct)
....other security procedures. The advantage of the methods is that available general software is often straightforward to apply. The associated research problems relate to how seriously analytic properties are compromised. Additive noise is known to preserve some of the analytic properties of files (Kim 1986, 1989; Fuller 1993) Research problems are whether general software can be developed and whether files are free of disclosures. Combinations of additive noise and limited swapping have been used by Kim and Winkler (1995) and Winkler (1998) Data perturbation methods (Tendick and Matloff 1994) are ....
Kim, J. 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.
Views On The Production And Use Of Confidential Microdata - Winkler (Correct)
....give out public use files. When agencies first produce the public use microdata, they should check it for analytic validity. Some methods of producing the public use data may have more of a tendency to yield analytically valid files and make checking for analytic validity easier. Additive noise (Kim 1986, 1990) and the generalizations (Sullivan and Fuller 1990, 1991; Fuller 1993, Little 1993) have a strong tendency to produce analytically valid files. The files that are produced via noise addition have generally not been confidential because a (possibly) small percentage of records can be ....
Kim, J. 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.
Producing Public-Use Microdata That Are Analytically Valid And.. - Winkler (1997) (Correct)
.... from simple suppression of names, addresses, and unique identifiers such as Social Security Number (SSN) to truncation of large values or other outliers, to data swapping (Dalenius and Reiss 1982) to suppression (DeWaal and Willenborg 1996) and finally to sophisticated methods of data masking (Kim 1986, Sullivan and Fuller 1990, Fuller 1993, Kim and Winkler 1995, Fienberg 1997) Rather than just provide publicly released microdata that have the same means and a few other properties of the confidential microdata, the sophisticated methods are intended to yield microdata that can be used for ....
....need to be used in correctly matching different pairs of records. These modern record linkage methods are often in commercially available code that can be applied by relatively naive users in re identification experiments. With the more sophisticated ways of producing publicuse microdata (e.g. Kim 1986, Fuller 1993, Kim and Winkler 1995, DeWaal and Willenborg 1996) re identification is considerably more difficult but possible if the individual performing the work is experienced in record linkage and able to write certain types of sophisticated computer code. At some point in the near future, ....
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Kim, J. 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.
Masking Microdata Files - Kim, Winkler (1995) (5 citations) Self-citation (Kim) (Correct)
....policy regarding earned income credit and other benefits. The microdata is masked in such a manner that both Bureau of the Census and IRS confidentiality restrictions are met. No masked IRS quantitative data can alone be used in reidentifications. The main methodology is an additive noise approach (Kim 1986) for masking multivariate normal data that preserves confidentiality and can preserve many essential characteristics of the data such as means, variances, and correlations. The CPS and IRS data of the application are known to be approximately 2 multivariate normal. While the methodology has ....
....it is easier to minimize the chance of reidentification. We primarily need be concerned with higher income individuals or those with distinct characteristics that might be easily identified even when sampling rates are low. 2.2. Masking Methodology Masking is via an additive noise approach (Kim 1986, see also Sullivan and Fuller 1989, Sullivan and Fuller 1990, and Little 1993) Adding random noise with the same correlation structure as the original unmasked data is currently the only method (Little 1993) that preserves correlations. Appendix A.3 allows us to determine means and covariances ....
Kim, J. 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.
Microdata Disclosure Risk in Database Privacy Protection - Crises (2004) (Correct)
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J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the Section on Survey Research Methods, pages 303--308, Alexandria VA, 1986. American Statistical Association. 10
Comparing SDC Methods for Microdata on the Basis of.. - DOMINGO-FERRER.. (Correct)
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Kim, J. J., (1986), A method for limiting disclosure in microdata based on random noise and transformation, in Proc. of the ASA Sect. on Survey Res. Meth., pp. 303-308.
STATISTICAL COMMISSION and COMMISSION OF THE - Economic Commission For (2001) (Correct)
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Kim, J. J., (1986), A method for limiting disclosure in microdata based on random noise and transformation, in Proc. of the ASA Sect. on Survey Res. Meth., pp. 303-308.
Perturbative Masking for Microdata Privacy Protection in.. - Crises (2004) (Correct)
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J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the Section on Survey Research Methods, pages 303--308, Alexandria VA, 1986. American Statistical Association.
Outlier Protection in Continuous Microdata Masking - Mateo-Sanz, Sebé.. (2004) (Correct)
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J. J. Kim, "A method for limiting disclosure in microdata based on random noise and transformation", in Proc. of the ASA Sect. on Survey Res. Meth., pp.303-308, 1986.
Non-Perturbative Methods for Microdata Privacy in Statistical.. - Crises (2004) (Correct)
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J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the Section on Survey Research Methods, pages 303--308, Alexandria VA, 1986. American Statistical Association.
On the Security of Noise Addition for Privacy in.. - Domingo-Ferrer.. (Correct)
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J. J. Kim, "A method for limiting disclosure in microdata based on random noise and transformation", in Proceedings of the Section on Survey Research Methods. American Statistical Association, 1986, pp. 303-308.
Comparing SDC Methods for Microdata - On The Basis (Correct)
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Kim, J. J., (1986), A method for limiting disclosure in microdata based on random noise and transformation, in Proc. of the ASA Sect. on Survey Res. Meth., pp. 303-308.
Microaggregation for Privacy Protection in Statistical Databases - Crises (2004) (Correct)
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J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the Section on Survey Research Methods, pages 303--308, Alexandria VA, 1986. American Statistical Association.
Watermarking for Multilevel Access to Statistical.. - Domingo-Ferrer.. (2001) (Correct)
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J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the ASA Section on Survey Research Methods, 1986, pp. 303-308.
Information Loss Measures for Microdata in Database Privacy.. - Crises (2004) (Correct)
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J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the Section on Survey Research Methods, pages 303--308, Alexandria VA, 1986. American Statistical Association.
Additive Noise for Microdata Privacy Protection in Statistical.. - Crises (2004) (Correct)
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J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the Section on Survey Research Methods, pages 303--308, Alexandria VA, 1986. American Statistical Association.
Privacy Preserving Database Application Testing - Wu, Wang, Zheng (2003) (2 citations) (Correct)
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J.J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the section on survey research methods, American Statistical Association, 1986.
Trading Off Information Loss and Disclosure Risk in Database.. - Crises (2004) (Correct)
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J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the Section on Survey Research Methods, pages 303--308, Alexandria VA, 1986. American Statistical Association.
Synthetic Microdata Generation for Database Privacy Protection - Crises (2004) (Correct)
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J. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the Section on Survey Research Methods, pages 303--308, Alexandria VA, 1986. American Statistical Association.
Privacy Preserving Database Application Testing - Wu, Wang, Zheng (2003) (2 citations) (Correct)
No context found.
J.J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the section on survey research methods, American Statistical Association, 1986.
Re-identification Methods for Masked Microdata - Winkler (2004) (6 citations) (Correct)
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Kim, J. J. A Method for Limiting Disclosure in Microdata Based on Random Noise and Transformation, American Statistical Association, Proceedings of the Section on Survey Research Methods, (1986) 303-308
Interval Computations Related to Privacy in Statistical.. - Longpre, Kreinovich (2002) (Correct)
No context found.
J. Kim, "A method for limiting disclosure of microdata based on random noise and transformation", Proceedings of the Section on Survey Research Methods of the American Statistical Association, 1986, pp. 370--374.
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