Verification in Privacy Preserving Data Publishing

Abstract

Privacy preserving data publication is a major concern for both the owners of data and the data publishers. Principles like k-anonymity, l-diversity were proposed to reduce privacy violations. On the other side, no studies were found on verification on the anonymized data in terms of adversarial breach and anonymity levels. However, the anonymized data is still prone to attacks due to the presence of dependencies among quasi-identifiers and sensitive attributes. This paper presents a novel framework to detect the existence of those dependencies and a solution to reduce them. The advantages of our approach are i) privacy violations can be detected, ii) the extent of privacy risk can be measured and iii) re-anonymization can be done on vulnerable blocks of data. The work is further extended to show how the adversarial breach knowledge eventually increased when new tuples are added and an on the fly solution to reduce it is discussed. Experimental results are reported and analyzed.