An Integrative Approach for Measuring Privacy Impact of Identifiers in the Automotive Domain

Abstract

Information technology is commonly used in automotive applications, and has introduced associated opportunities and threats. At the same time, the dissemination and use of certain privacy-sensitive data (i.e., identifying data) continues to increase, raising serious questions about privacy and anonymity. However, the effect of identifying data on privacy depends on various aspects, such as their basic structure. We propose that the preemptive assessment of privacy levels is a key factor for reliable privacy processes in vehicular development, extending the existing assessment during runtime. Thus, we identify a comprehensive and classified set of privacy indicators for identifiers, and explore the possible application of a single indicator by proposing privacy impact metrics that are based on entropy. We demonstrate the feasibility of our approach using a real dataset of vehicle identification numbers (VINs).