Abstract
We study the problem of privacy preservation in multiple independent data publishing. An attack on personal privacy which uses independent datasets is called a composition attack. For example, a patient might have visited two hospitals for the same disease, and his information is independently anonymized and distributed by the two hospitals. Much of the published work makes use of techniques that reduce data utility as the price of preventing composition attacks on published datasets. In this paper, we propose an innovative approach to protecting published datasets from composition attack. Our cell generalization approach increases both protection of individual privacy from composition attack and data utility. Experimental results show that our approach can preserve more data utility than the existing methods.
Highlights
Data sharing helps the individual researcher and research organizations to run data analytics operations on published databases
Personal privacy is ensured by privacy-preserving data publishing methods and anonymization of the data at the time of widespread publication
We present the anonymization algorithm which can successfully anonymize the dataset to ensure the protection from composition attack and increase the data utility as well
Summary
Data sharing helps the individual researcher and research organizations to run data analytics operations on published databases. The publishing of data may jeopardize personal privacy and disclose the sensitive values [1]. Personal privacy is ensured by privacy-preserving data publishing methods and anonymization of the data at the time of widespread publication. A sender transfers his dataset to a recipient where an attacker will not be able to gain any knowledge about that dataset This privacy setting is followed for the privacy-preserving data publishing event. I.e., adversary receives the published data, and he might use previously gained background knowledge to identify a person by linking with some publicly available data sources [6]. Background knowledge helps the adversary to learn relevant sensitive information from the published microdata tables. Background knowledge helps in finding records and breaching individual privacy in published microdata tables. The adversary can use the QI values of the person, that is person P in the table T, and the following facts to breach the sensitive value S:
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