T he ability to collect and disseminate individually identifiable microdata is becoming increasingly important in a number of arenas. This is especially true in health care and national security, where this data is considered vital for a number of public health and safety initiatives. In some cases legislation has been used to establish some standards for limiting the collection of and access t...

Disclosure limitation methods for protecting the confidentiality ofrespondents in survey microdata often use perturbative techniques whichintroduce measurement error into the categorical identifying variables. Inaddition, the data itself will often have measurement errors commonly arisingfrom survey processes. There is a need for valid and practical ways to assess theprotect...

The goal of privacy protection in statistical databases is to balance the social right to know and the individual right to privacy. When microdata (i.e. data on individual respondents) are released, they should stay analytically useful but should be protected so that it cannot be decided whether a published record matches a specific individual. However, there is some uncertainty in the assessme...

The development of new disclosure protection techniques is useful only insofar as those techniques are adopted by statistical agencies. In order for technical experts in disclosure limitation to be successful, they are likely to need to interact with the appropriate statistical offices. This paper discusses just such a successful interaction in the United States. It describes the foundation tha...

Privacy-preserving microdata publishing currently lacks a solid theoretical foundation. Most existing techniques are developed to satisfy syntactic privacy notions such as k-anonymity, which fails to provide strong privacy guarantees. The recently proposed notion of differential privacy has been widely accepted as a sound privacy foundation for statistical query answering. However, no general p...

Microdata protection in statistical databases has recently become a major societal concern and has been intensively studied in recent years. Statistical Disclosure Control (SDC) is often applied to statistical databases before they are released for public use. Microaggregation for SDC is a family of methods to protect microdata from individual identification. SDC seeks to protect microdata in s...

In previous work by Domingo-Ferrer et al., rank swapping and multivariate microaggregation has been identified as well-performing masking methods for microdata protection. Recently, Dandekar et al. proposed using synthetic microdata, as an option, in place of original data by using Latin hypercube sampling (LHS) technique. The LHS method focuses on mimicking univariate as well as multivariate s...

The paper discusses how a statistical office could strike a satisfactory balance between confidentiality protection and freedom of information. Flexible use of statistical data is of vital interest for researchers and for the democratic process. On the other hand, the willingness of respondents to provide data is dependent on the ability of the statistical office to guarantee their anonymity. T...

Objective Secondary data analysis is becoming more powerful and commonly utilized for biomedical research using patient records and genomic data. In both data, de-identification has been proven to be ineffective due to linkage attacks that can re-identify some subpopulation of the data. We need a better model for privacy protection in secondary analysis of biomedical data. Design In this paper,...