Big Data with Measurable Security and Privacy with Application in the Field of Smart Cars
CRISP IP3.2 – Part of CRISP
Period: 2017 – 2020
Support: see right
The project aims to develop methods for the collection, aggregation and evaluation of vehicle operating data, which at the same time allow protection of individual privacy. For this purpose, three exemplary applications are being investigated: “software-based lifetime monitoring” in the area of lightweight construction, the synthesis of representative driving driving cycles in the field of emissions/efficiency and geodata acquisition for the transmission of local dangers in the field of autonomy. All applications will be assessed on the data they need to enable the implementation based on aggregated-data technical and how sensitive information about individuals can be protected.
The focus of IMS research in the project is on methods that enable a stochastic synthesis of short and representative driving cycles based on aggregated fleet data. The methods work multidimensionally and can consider the interdependency of multiple signals in realistic test cycles. For example, not only the speed specification plays a decisive role for the consumption assessment of vehicles (which is what classic driving cycles limit), but also the slope profile and the outside temperature, among other things.
The user and his / her privacy are an essential aspect. Therefore, possibilities for the aggregation of the operating data before the storage are examined, which maintain technical information and at the same time suppress privacy-critical data. The goal is to enable technical applications with the least possible restriction of privacy.
Contakct: Arved Eßer
Further informationen you can find on the Project homepage.
Selected publications: Enabling a Privacy-Preserving Synthesis of Representative Driving Cycles from Fleet Data using Data Aggregation, Process for the Validation of Using Synthetic Driving Cycles Based on Naturalistic Driving Data Sets