Statistical disclosure control in tabular data

Data disseminated by National Statistical Agencies (NSAs) can be classified as either microdata or tabular data. Tabular data is obtained from microdata by crossing one or more categorical variables. Although cell tables provide aggregated information, they also need to be protected. This chapter is a short introduction to tabular data protection. It contains three main sections. The first one shows the different types of tables that can be obtained, and how they are modeled. The second describes the practical rules for detection of sensitive cells that are used by NSAs. Finally, an overview of protection methods is provided, with a particular focus on two of them: “cell suppression problem” and “controlled tabular adjustment”.Postprint (published version

A genetic approach to statistical disclosure control

Statistical disclosure control is the collective name for a range of tools used by data providers such as government departments to protect the confidentiality of individuals or organizations. When the published tables contain magnitude data such as turnover or health statistics, the preferred method is to suppress the values of certain cells. Assigning a cost to the information lost by suppressing any given cell creates the cell suppression problem. This consists of finding the minimum cost solution which meets the confidentiality constraints. Solving this problem simultaneously for all of the sensitive cells in a table is NP-hard and not possible for medium to large sized tables. In this paper, we describe the development of a heuristic tool for this problem which hybridizes linear programming (to solve a relaxed version for a single sensitive cell) with a genetic algorithm (to seek an order for considering the sensitive cells which minimizes the final cost). Considering a range of real-world and representative artificial datasets, we show that the method is able to provide relatively low cost solutions for far larger tables than is possible for the optimal approach to tackle. We show that our genetic approach is able to significantly improve on the initial solutions provided by existing heuristics for cell ordering, and outperforms local search. This approach is then extended and applied to large statistical tables with over 200000 cells. © 2012 IEEE

K−means clustering microaggregation for statistical disclosure control

This paper presents a K-means clustering technique that satisfies the bi-objective function to minimize the information loss and maintain k-anonymity. The proposed technique starts with one cluster and subsequently partitions the dataset into two or more clusters such that the total information loss across all clusters is the least, while satisfying the k-anonymity requirement. The structure of K− means clustering problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the K− means clustering algorithm is compared against the most recent microaggregation methods. Experimental results show that K− means clustering algorithm incurs less information loss than the latest microaggregation methods for all of the test situations

A posteriori disclosure risk measure for tabular data based on conditional entropy

Statistical database protection, also known as Statistical Disclosure Control (SDC), is a part of information security which tries to prevent published statistical information (tables, individual records) from disclosing the contribution of specific respondents. This paper deals with the assessment of the disclosure risk associated to the release of tabular data. So-called sensitivity rules are currently being used to measure the disclosure risk for tables. These rules operate on an a priori basis: the data are examined and the rules are used to decide whether the data can be released as they stand or should rather be protected. In this paper, we propose to complement a priori risk assessment with a posteriori risk assessment in order to achieve a higher level of security, that is, we propose to take the protected information into account when measuring the disclosure risk. The proposed a posteriori disclosure risk measure is compatible with a broad class of disclosure protection methods and can be extended for computing disclosure risk for a set of linked tables. In the case of linked table protection via cell suppression, the proposed measure allows detection of secondary suppression patterns which offer more protection than others

Assessing the disclosure risk of CTA-like methods

Minimum distance controlled tabular adjustment (CTA) is a recent perturbative approach for statistical disclosure control in tabular data. CTA looks for the closest safe table, using some particular distance. In this talk we provide empirical results to assess the disclosure risk of the method. A set of 33 instances from the literature and four different attacker scenarios are considered. The result s show that, unless the attacker has good information about the original table, CTA has low disclosure risk. This talk summarizes results reported in the paper “Castro, J. (2013). On assessing the disclosure risk of controlled adjustment methods for statistical tabular data, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 20, 921–941.Peer ReviewedPostprint (published version