Trajectory Data Anonymization Using Profile-Based Privacy Risk Estimation
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Solution Overview
Problem
Existing technologies struggle to effectively balance the provision of location-based services with maintaining user anonymity, as anonymization algorithms often render data less accurate and fail to consider the specific characteristics of mobility data, leading to privacy risks.
Innovation Solution
A method and apparatus for estimating the privacy level of anonymized trajectory data by determining a dataset profile, selecting an anonymization strategy based on parameters like mobility data density and geographical area type, and using Maximum Privacy Loss to assess the privacy risk, allowing for re-anonymization if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anonymization algorithms are applied to trajectory data, then user anonymity is improved, but data accuracy deteriorates
Solution Approach 1:
The patent applies different anonymization strategies to different segments of trajectory data based on their specific characteristics. High-density urban area trajectories receive stronger anonymization than low-density rural area trajectories, allowing each region to maintain appropriate privacy protection without unnecessarily degrading overall data accuracy.
Solution Approach 2:
The system dynamically adjusts anonymization parameters such as k-anonymity thresholds, spatial grouping radii, and temporal aggregation intervals based on dataset profile parameters including mobility data density, road functional class, and geographical area type. This enables optimization of the privacy-accuracy trade-off for each specific dataset.
2Ease of operation
If generic anonymization algorithms are used, then processing simplicity is improved, but privacy risk increases
Solution Approach 1:
The patent transforms the anonymization process from a one-size-fits-all approach to a parameter-driven adaptive system. By profiling datasets according to mobility data density, road functional class, and geographical area type, the system automatically selects and adjusts anonymization parameters to achieve appropriate privacy protection for each specific context.
Solution Approach 2:
The system incorporates privacy risk estimation as feedback to the anonymization process. By evaluating the anonymized output against re-identification risks and adjusting parameters accordingly, the system ensures that simplicity does not compromise privacy protection.
Data Source
AI summary
Embodiments described herein relate estimating a privacy level of an anonymized dataset based on an anonymization strategy selected using parameters of the dataset that form a dataset profile. Methods may include: receiving a dataset defining at least one trajectory; determining parameters of the dataset; generating a profile of the dataset based on the parameters of the dataset to establish a dataset profile; identifying a predetermined profile corresponding to the dataset profile; determining an anonymization strategy corresponding to the predetermined profile; anonymizing the dataset using the anonymization strategy to generate an anonymized dataset; and publishing the anonymized dataset for use with location-based services. According to some embodiments, identifying the predetermined profile corresponding to the dataset profile includes identifying a predetermined profile having parameters within a predefined degree of similarity of the parameters of the dataset.