Nowadays, the need for privacy motivates the use of methods that permit us to protect a microdata file both minimizing the disclosure risk and preserving the statistical utility. Nevertheless, research is usually focused on how data utility is preserved, and much less research effort is dedicated to the study of the tools that an intruder might use to compromise the privacy of the data or, in o...

Microaggregation for Statistical Disclosure Control (SDC) is a family of methods to protect microdata from individual identification. SDC seeks to protect microdata in such a way that can be published and mined without providing any private information that can be linked to specific individuals. The aim of SDC is to modify the original microdata in such a way that the modified data and the orig...

Blocking is a well-known technique used to partition a set of records into several subsets of manageable size. The standard approach to blocking is to split the records according to the values of one or several attributes (called blocking attributes). This report presents a new blocking method based on 2-trees for intelligently partitioning very large data sets. Blocking makes sense whenever th...

Microaggregation is an efficient Statistical Disclosure Control (SDC) perturbative technique for microdata protection. It is a unified approach and naturally satisfies k-Anonymity without generalization or suppression of data. Various microaggregation techniques: fixed-size and data-oriented for univariate and multivariate data exists in the literature. These methods have been evaluated using t...

Statistical agencies release microdata from social surveys as public-use files after applying statistical disclosure limitation (SDL) techniques. Disclosure risk is typically assessed in terms of identification risk, where it is supposed that small counts on cross-classified identifying key variables, i.e., a key, could be used to make an identification and confidential information may be learn...

In this paper we discuss how most edit constraints can be taken into account in an effective way through microaggregation. We discuss different edit constraints and some variations of microaggregation that permits to deal with such constraints. We will also present our software to formalize and deal with such constraints in an automatic way.

Social networks have become an essential part of the people?s communication system. They allow the users to express and share all the things they like with all the people they are connected with. However, this shared information can be dangerous for their privacy issues. In addition, there is some information that is not explicitly given but is implicit in the text of the posts that the user sh...

Many smartphone-based applications need microdata, but publishing a microdata table may leak respondents’ privacy. Conventional researches on privacy-preserving data publishing focus on providing identical privacy protection to all data requesters. Considering that, instead of trapping in a small coterie, information usually propagates from friend to friend. The authors study the privacy-preser...

We describe and analyze a method that blends records from both observed and synthetic microdata into public-use tabulations on establishment statistics. The resulting tables use synthetic data only in potentially sensitive cells. We describe different algorithms, and present preliminary results when applied to the Census Bureau's Business Dynamics Statistics and Synthetic Longitudinal Business ...