Behind the Mix-Zones Scenes: On the Evaluation of the Anonymization Quality

Abstract

In the flowering of ubiquitous computing, networks like the Internet of Things and the Internet of Vehicles have contributed to connecting objects and sharing location services in broad environments like smart cities bringing many benefits to citizens. However, these services yield massive and unrestricted mobility data of citizens that pose privacy concerns, among them recovering the identity of citizens with linking attacks. Although several privacy mechanisms have been proposed to solve anonymization problems, there are few studies about their behavior and analysis of the data quality anonymized by these techniques. In this paper, we introduce the anonymization quality. For this, we propose analyzing the mix-zones metrics Amount of Cars on Mix-zone (ACM), Interval of Arrival Time between Cars on Mix-zones (ITM), and Activation Time of the Mix-zone (ATM) for characterizing and evaluating the impacts of anonymization quality over time and space in mobility data. The results showed that mix-zone metrics reflect anonymization behavior and can measure the anonymization quality over time. This insight can contribute significantly to building privacy proposes based on anonymization more effectively than based only on traffic flow. To our knowledge, this is the first work that uses mix-zones metrics analysis to observe the anonymization behavior in quality terms.