Privacy risks of ranked data publication

Official URL: http://risc01.sabanciuniv.edu/record=b2057961 (Table of Contents)

In recent years, data privacy has become a major concern for data owners who share information on private databases. In order to deal with this issue, data owners employ various mitigation strategies including disclosing partial information on datasets (i.e., mean, median, histograms) or obfuscating the private attributes in a way that keeps a balance between data privacy and utility. However, such methods have failed to preserve privacy under certain adversary models. As an example, distance preserving transforms are found to be vulnerable to attacks in which adversary has access to few known records in the database. In this work, we similarly analyze the privacy implications of rank publication of data records based on the output of a ranking function. While much research has gone in the design of a ranking function, analyzing privacy issues of database rankings is still a novel problem. Many real world website reveal ranking of data records assuming that ranking itself is not privacy sensitive. Examples of such rankings are evaluations of universities, jobs, bank credit applications and hospital statistics on various categories. Our work shows that seemingly naive information about rankings can cause severe privacy leakages. In particular, we show that an adversary with a few known samples from the private data can infer about the actual attributes of an unknown record by utilizing the ranking information.