Statistical disclosure control refers to the methodology used in the design of the statistical outputs from a survey for protecting the confidentiality of respondents’ answers. The threat to confidentiality is assumed to come from a hypothetical intruder who has access to these outputs and seeks to use them to disclose information about a survey respondent. One key concern relates to identity disclosure, which would occur if the intruder were able to link a known individual (or other unit) to an element of the output. Another main concern relates to attribute disclosure, which would occur if the intruder could determine the value of some survey variable for an identified individual (or other unit) using the statistical output. Measures of the probability of disclosure are called disclosure risk. If this level of risk is deemed unacceptable then it may be necessary to apply a method of statistical disclosure control to the output. The choice of which method and how much protection to apply depends not just on the impact on disclosure risk but also on the impact on the utility of the output to users. This paper provides a review of statistical disclosure control methodology for two main types of survey output: (i) tables of estimates of population parameters and (ii) microdata, often released as a rectangular file of variables by analysis units. For each of these types of output, the definition and estimation of disclosure risk is discussed as well as methods for statistical disclosure control.