A survey on statistical disclosure control and micro-aggregation techniques for secure statistical databases

This paper surveys the fields of Statistical Disclosure Control (SDC) and Micro-Aggregation Techniques (MATs), which are both areas fundamental to the science of secure Statistical DataBases (SDBs). The paper is written from the perspective of a computer scientist with the hope that it will prove to be a source of reference material useful to researchers and practitioners in the field. The paper first introduces the concept of SDC and describes the domain of its applications and the various data types that are currently used in SDBs. It then proceeds to focus on the family of micro-data types in SDBs. At this juncture, we introduce the importance of the relevant measures, namely the metrics termed as the Information Loss (IL) and the Disclosure Risk (DR), after which we survey the various methods of resolving the conflicting goals that these metrics represent. Thereafter, the paper summarizes the perturbative and non-perturbative SDC methods for micro-data protection, and it focuses on the families of MATs by formally stating the Micro-Aggregation Problem and surveying it in a comprehensive manner. Apart from the paper including a historical view of the field of MATs, it describes a broad selection of work that has been reported more recently. Indeed, we believe that this paper represents a complete overview of the state-of-the-art techniques. Copyrigh