Trajectory Cropping Heuristics for Privacy-Aware Data Anonymization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Location-based service providers face challenges in accessing accurate trajectory data while ensuring user privacy, as highly accurate data can reveal sensitive information, and existing methods struggle to align with user-defined privacy preferences.
Innovation Solution
A system that evaluates and selects cropping heuristics for trajectory data based on user-defined privacy preferences, using a framework that considers computational cost, utility, and alignment with user expectations, to anonymize data by determining appropriate points for cropping, such as speed, distance, and map features, ensuring privacy protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly accurate trajectory data is collected and shared, then service accuracy and relevance are improved, but user privacy is compromised
Solution Approach 1:
The patent extracts and removes sensitive portions of trajectory data through cropping operations. By identifying and removing the initial and final segments of trajectories that contain sensitive location information (such as home and work addresses), the system retains only the necessary intermediate portions for service provision, thereby maintaining service accuracy while protecting user privacy.
Solution Approach 2:
The patent changes the parameters of trajectory data by applying cropping heuristics that modify the temporal and spatial extent of collected data. Through parameters such as cropping duration, distance thresholds, and speed-based criteria, the system transforms raw high-accuracy trajectory data into anonymized data that preserves service utility while eliminating privacy risks.
2Object-affected harmful factors
If trajectory data is cropped to protect privacy, then privacy protection is improved, but data accuracy and utility are reduced
Solution Approach 1:
The patent employs dynamic cropping heuristics that adapt to varying conditions rather than applying fixed cropping rules. By considering real-time factors such as user behavior patterns, environmental context, service requirements, and mobility characteristics, the system dynamically adjusts cropping parameters to minimize data loss while maximizing privacy protection, thereby maintaining trajectory data utility.
Solution Approach 2:
The patent systematically adjusts multiple parameters including cropping duration, distance thresholds, speed criteria, and temporal windows to optimize the balance between privacy protection and data accuracy. Through parameter tuning and optimization, the system identifies the optimal cropping configuration that achieves sufficient anonymization while preserving the essential characteristics needed for service provision.
3Adaptability or versatility
If multiple cropping heuristics are evaluated and selected based on user preferences, then alignment with user expectations is improved, but computational complexity increases
Solution Approach 1:
The patent segments the evaluation process into distinct modular components: data collection modules that gather user preferences and context, evaluation modules that assess multiple cropping heuristics against these preferences, and selection modules that choose the optimal heuristic. This segmentation allows the system to handle complexity through organized, reusable components that can be independently developed and tested.
Solution Approach 2:
The patent manages computational complexity by parameterizing the evaluation framework, allowing flexible adjustment of evaluation criteria, heuristic candidates, and preference weights. Through parameter optimization and configuration, the system can adapt the evaluation process to different service contexts and user profiles without requiring complete redesign, thereby managing complexity while maintaining versatility.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An approach is disclosed for a data-driven evaluation of heuristics for trajectory cropping. The approach involves, e.g., determining a cropping heuristic, wherein the cropping heuristic comprises an algorithm for cropping a probe trajectory collected from one or more sensors of a mobile device to anonymize the probe trajectory data. The approach also involves processing the probe trajectory using the cropping heuristic to generate a cropped probe trajectory. The approach further involves extracting one or more heuristic-based features of the cropped probe trajectory data. The approach also involves extracting one or more privacy-based features from a privacy preference, wherein the one or more privacy-based features represent a target level of cropping to meet the privacy preference. The approach further involves computing a score based on a distance between the one or more heuristic-based features and the one or more privacy-based features. The approach further involves providing the score as an output.