Privacy disclosure by de-anonymization using music preferences and selections

In the current digital era, we continuously create records of our activities, that are accumulated in a variety of data-storages. One common way to protect our privacy is to remove identifiers (e.g., ID, name) from the records. This approach is known to be naive, as in many cases re-identification is enabled based on quasi-identifiers (e.g., age, gender). In this research we examine an interesting and unexpected new quasi-identifier – music selections of an individual which represents their musical preferences. In the current era we consume music mainly on-demand by streaming (e.g., Spotify, YouTube, Apple Music) rather than as broadcast. The prosperity of the various music platforms is immense, and so is the sharing of beloved music, for example on online social networks. Thus, the creation of records that represent music selections is prevalent. In this paper we introduce a methodology to re-identify users based on their music selections, and prove the efficiency of the methodology empirically in four experiments (n=22,38,35,30). We discuss the social and emotional benefits of the current way we listen to music, against the threat of privacy disclosure.