A literature survey and classifications on data deanonymisation

Abstract

The problem of disclosing private anonymous data has become increasingly serious particularly with the possibility of carrying out deanonymisation attacks on publishing data. The related work available in the literature is inadequate in terms of the number of techniques analysed, and is limited to certain contexts such as Online Social Networks. We survey a large number of state-of-the-art techniques of deanonymisation achieved in various methods and on different types of data. Our aim is to build a comprehensive understanding about the problem. For this survey, we propose a framework to guide a thorough analysis and classifications. We are interested in classifying deanonymisation approaches based on type and source of auxiliary information and on the structure of target datasets. Moreover, potential attacks, threats and some suggested assistive techniques are identified. This can inform the research in gaining an understanding of the deanonymisation problem and assist in the advancement of privacy protection.