Automatic Generation of Masked Microdata

Disclosure Control is the discipline concerned with the modification of data containing confidential information about individual entities, such as persons, households, businesses, etc. in order to prevent third parties working with these data from recognizing entities in the data and thereby disclosing information about these entities. In very broad terms, disclosure risk is the risk that a given form of disclosure will occur if a masked microdataset is released. Microdata represents a series of records, each record containing information on an individual unit. Several microdata disclosure control frameworks exist in literature but they focus on specific disclosure problems. Our proposed framework attempts to define the microdata disclosure control problem more generally. In this paper we describe the architecture of a software system called AMMG (Automatic Masked Microdata Generator). The system will generate masked microdata with low disclosure risk and information loss. A general framework for microdata disclosure control is proposed for this system. Also, existing disclosure risk measures are extended by this research. Variables in the microdata are classified at two-levels, one specified by the data owner and the other indicating the knowledge states of potential data intruders. These classifications form the basis for organizing disclosure risk scenarios. The disclosure risk measure presented in this paper is validated in our illustrations.