Geographically intelligent disclosure control for flexible aggregation of census data

Geographically Intelligent Disclosure Control for Census Data Production

A method for assessing risk of disclosure in census microdata and tabular data

The area of con dentiality in personal databases is one which is currently of considerable interest. This paper concentrates on current research relating to measuring the risk of disclosure of information about individuals in census data, which the authors believe is directly applicable to other data sources, such as employer-employee databases. Various methods have been used in the past by cen...

Disclosure Risk and Sample of Anonymized Records

The disclosure problem relates to the possibility of identifying individuals in the released statistical information . The paper evaluates the disclosure risk on a 3% sample of individual data from the Slovene 1991 Population Census . The concept of uniqueness is used for this purpose . The level of regional aggregation, the number of identifying variables and the grouping of the categories are...

Statistical Disclosure Control Methods for Census Frequency Tables

This paper provides a review of common statistical disclosure control (SDC) methods implemented at Statistical Agencies for standard tabular outputs containing whole population counts from a Census (either enumerated or based on a register). These methods include record swapping on the microdata prior to its tabulation and rounding of entries in the tables after they are produced. The approach ...

Statistical Disclosure Control: New Directions and Challenges

Traditionally, statistical agencies generally release outputs in the form of microdata and tabular data. Microdata contain data from social surveys and tabular data contain either frequency counts, such as for census dissemination, or magnitude data typically arising from business surveys, eg. total revenue. For each of these traditional outputs, there has been much research on how to quantify ...