A Dynamic Privacy Tuning Mechanism in Mix-Zones

In the age of the Internet of Things and the Internet of Vehicles, mobile entities generate massive and unrestricted trajectory data that pose privacy concerns, such as linking attacks. Mix-zones have been used to anonymize citizens' identities to address privacy issues. However, the mix-zones depend on factors, such defining fair privacy levels, that impact their performance, privacy level, and anonymization quality. This work proposes the Dynamic- $k$ Mix-zone ($\mathbf{D} k$-Mix), a dynamic mix-zone that tunes the privacy level over time in an online model with linear complexity, according to vehicles' traffic fluctuations, to achieve higher anonymization. We evaluated our approach on real and synthetic datasets, comparing it against two privacylevel prediction mechanisms and classical mix-zones regarding coverage, privacy metrics, and Anonymization Quality (AQ). Results showed that $\mathbf{D} k$-Mix achieves more accurate privacylevel estimation, with efficacy, anonymization rate, and AQ comparable to the best classical mix-zones. Moreover, it maximized privacy over time while minimizing trajectory re-identification up to 30.97 % and 40.92 % for high and low traffic, outperforming traditional mix-zones.