Car insurance is one of the most important and profitable markets within the property and casualty insurance industry. Therefore, there is a great interest in accurate pricing, both for insurance companies and policyholders. This study examines a pricing model (i.e., personalized car insurance) based on drivers' driving patterns and styles. Since the model relies on the collection of personal information, driver privacy becomes a significant factor. The purpose of the study was to map and measure car drivers' privacy concern, and the parameters influencing them, towards personalized car insurance, focusing on privacy and collection of personal data. To investigate the study's purpose, a quantitative study was conducted, and a theoretical model was developed based on the Privacy calculus theory. The model consisted of the following factors: Perceived benefit, Perceived risk, Privacy concern, and Intent to disclose information. The study was carried out through a survey, with the sample selected through convenience sampling. The survey was distributed through the social platform Facebook. The data was analyzed using bivariate and multivariate statistics. Results showed that perceived benefit and perceived risk have a moderate impact on the privacy concern, and privacy concern has a moderate impact on intent to disclose information. Based on the results, the following conclusions could be drawn: both perceived benefit and perceived risk have some influence on car drivers' privacy concern towards personalized car insurance, and the privacy concern towards personalized car insurance has a moderate impact on how a potential implementation would be accepted.