Re-identification Attack based on Few-Hints Dataset Enrichment for Ubiquitous Applications

Abstract

Ubiquitous and pervasive applications record a large amount of data about users, to provide context-aware and tailored services. Although this enables more personalized applications, it also poses several questions concerning the possible misuse of such data by a malicious entity, which may discover private and sensitive information about the users themselves. In this paper we propose an attack on ubiquitous applications pseudo-anonymized datasets which can be leaked or accessed by the attacker. We enrich the data with true information which the attacker can obtain from a multitude of sources, which will eventually spark a chain reaction on the records of the dataset, possibly re-identifying users. Our results indicate that through this attack, and with few hints added to the dataset, the possibility of re-identification are considerable, achieving more than 70% re-identified users on a public available dataset. We compare our proposal with the state of the art, showing the improved performance figures obtained thanks to the graph-modeling of the dataset records and the novel hint structure.