The Real-Time Anonymizer anonymizes location data for use in one or more use cases. Different use cases require different output data. So you can use and optimize the available anonymization methods to provide the maximum utility for a specific use case while privacy is protected. For example, the
TrafficInformation use case requires sets of continuous probe points containing no major gaps (within the set of probe points). For this use case, the
SplitAndGap anonymization method is suitable and the method is optimized to provide as many probe points as possible within all anonymized sets of probe points.
Raw location data is the input for anonymization. Different use cases have different data latency requirements. Real-time use cases (for real time traffic information) require that anonymized data is provided with a maximum latency of a couple of minutes. In this case, latency is the length of time (in minutes) from when data was collected by the vehicle to the point when data is processed and provided as a service (that is traffic information served to vehicles). Latency requirements for different use cases determine whether a complete journey (represented as a single trajectory , or a subset of the most recent points from a journey (chunk of a trajectory) can be anonymized in one go. The Real-Time Anonymizer works with real-time data only.
Anonymization is applied on real-time data, when a short latency is required. Real-time location data is a set of the most recent probe points and/or events from a single user journey covering a specific time period (between t=2 minutes and t=3 minutes). This set of probe points and events from a specific time period of a journey is a trajectory chunk. Trajectory chunks can cover different time periods for different journeys and for chunks of the same journey. The significant point is that with real-time data, additional chunks of a single journey are added over time. Normally, there will not be data for a whole journey input at a single point in time. Real-time use cases include hazard warning and traffic information.
When real-time location data is provided for anonymization, trajectory chunk data is required to have a consistent ID across all chunks belonging to the same trajectory. When this rule is not adhered to, each trajectory chunk is anonymized as a new trajectory and could have either no probe points anonymized or all probe points removed by start cutting. Remember to verify that trajectory IDs are being consistently used across trajectory chunks.
Raw location data trajectories can reveal sensitive information about persons. Anonymization methods are used to reduce this risk by removing or editing the information. The output data then differs from the input data that can include the following:
The split and gap anonymization method is an anonymization approach that works on real-time chunks, applying an anonymization strategy across all chunks of a single journey. The input trajectory chunk is split into zero or more anonymized sub-trajectories depending on the anonymization strategy configuration. Supported parameters include the following:
The output of this anonymization method is zero or more sub-trajectories, output as SENSORIS or SDII data messages to the output streaming layer. Each anonymized sub-trajectory has a new, random identifier not linked to the original trajectory ID.