Semantic Web Enabled Record Linkage Attacks on Anonymized Data

Big Data analytics holds the promise of enabling new discoveries in medicine, more efficient business practices, and other important advances. However, much of the data involved in such analyses contains personally identifiable information (PII) that needs to be removed or obscured prior to release in order to protect individuals’ privacy. Anonymizing a dataset is not as easy as it seems, however, and many supposedly anonymous datasets are vulnerable to a record linkage attack. Most of these attacks are currently conducted manually and can be labor-intensive, but as semantic web technologies continue to gain popularity, the potential for automating various aspects of these attacks increases. This paper explores the components of a record linkage attack and how semantic web technologies could play a role in facilitating them.