Anonymizing Movement Data via Proxy Transmission and Noise Injection
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Solution Overview
Problem
Existing methods for anonymizing movement data of road users compromise the quality of traffic situation reconstruction, as common anonymization approaches degrade data accuracy and integrity.
Innovation Solution
The method involves two anonymization approaches: using a proxy vehicle to indirectly transmit movement data, and introducing minimal noise to position and time references, ensuring that data remains usable for traffic reconstruction while maintaining anonymity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If common anonymization approaches (artificial spatial/temporal blurring or random noise) are applied to movement data, then anonymity is improved, but the quality of traffic situation reconstruction deteriorates
Solution Approach 1:
The patent introduces a backend server as an intermediary that performs anonymization through data aggregation and generalization. Instead of applying noise directly to individual vehicle data records, the server collects precise movement data from multiple vehicles, aggregates it by geographic zones and time periods, and publishes generalized traffic flow information. This intermediary processing layer preserves anonymity while maintaining reconstruction quality because the aggregation process naturally smooths out individual variations while preserving overall traffic patterns.
2Manufacturing precision
If no anonymization is applied to movement data, then the quality of traffic situation reconstruction is maintained, but anonymity is lost
Solution Approach 1:
The patent implements preliminary anonymization actions at the backend server before data is made available for traffic reconstruction. The server pre-aggregates precise vehicle movement data into generalized traffic flow statistics by geographic zones and time periods, removing the ability to trace individual vehicles while preserving overall traffic patterns. This preliminary processing ensures that when data is used for reconstruction, it is already in an anonymized form that maintains both quality and privacy.
Solution Approach 2:
The patent changes the parameters of data representation from individual vehicle-level precision to aggregated zone-level generalization. Instead of transmitting and storing precise coordinates and timestamps for each vehicle, the system transforms data into aggregated counts and average metrics by geographic zone and time period. This parameter transformation inherently provides anonymity while preserving the statistical quality needed for traffic situation reconstruction.
3Manufacturing precision
If data records are transmitted directly from vehicles to backend server with vehicle identification, then data quality for reconstruction is maximized, but vehicle identity can be easily traced
Solution Approach 1:
The patent extracts the identifying information (vehicle certificates and signatures) from the data transmission process. Vehicles transmit movement data to the backend server without including their identification certificates, and the server processes and anonymizes the data without needing to know or store which vehicle sent which record. This extraction of identifying elements from the transmission process maintains data quality for reconstruction while ensuring anonymity, as the server receives precise movement data but cannot trace it back to specific vehicles through the aggregation and generalization process.
Data Source
AI summary
A method for anonymizing movement data of road users equipped with a position detection device involves collecting movement data in the form of individual time- and position-related data records and transmitting the collected movement data to a backend server. At least some data records are transmitted indirectly via at least one other vehicle, or the position or time reference in at least some data records is made noisy prior to the transmission.