Trajectory Sub-Segmentation for Privacy-Preserving Mobility Data
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Solution Overview
Problem
Existing location-based services face a challenge in accurately collecting mobility data while preserving user privacy, as privacy-enhancing algorithms often reduce data utility.
Innovation Solution
Generate sub-trajectories based on changing points, introducing gaps to anonymize mobility data, ensuring that potential attackers cannot discern personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If privacy-enhancing algorithms are applied to mobility data, then user privacy is protected, but data accuracy is reduced
Solution Approach 1:
The patent segments the complete trajectory into multiple sub-trajectories by identifying changing points where the visibility context changes. Each sub-trajectory is processed independently with gap introduction, allowing privacy protection to be applied locally rather than globally, thereby preserving more utility while still protecting privacy.
Solution Approach 2:
The patent applies different processing strategies to different parts of the trajectory based on local visibility context. At changing points where privacy risk is higher, gaps are introduced; in other segments, the original data is preserved. This local differentiation allows the system to protect privacy where needed while maintaining data accuracy where possible.
2Object-affected harmful factors
If traditional anonymization techniques are used, then privacy is enhanced, but effectiveness against background knowledge attacks is insufficient
Solution Approach 1:
The patent performs preliminary analysis to identify changing points and calculate visibility contexts before applying anonymization. By pre-identifying where gaps should be introduced based on visibility analysis, the system proactively prevents background knowledge attacks rather than reacting to them after anonymization is applied.
Solution Approach 2:
The patent uses visibility context calculation as a feedback mechanism to determine where anonymization should be applied. The system continuously evaluates the visibility context at each point and adjusts the anonymization strategy accordingly, introducing gaps at changing points where visibility context changes, thereby improving resistance to background knowledge attacks.
3Object-affected harmful factors
If gaps are introduced in sub-trajectories, then privacy is protected, but data completeness is reduced
Solution Approach 1:
The patent segments the trajectory into sub-trajectories and only introduces gaps at specific changing points rather than uniformly across the entire trajectory. This selective segmentation minimizes information loss while still providing privacy protection where visibility context changes indicate potential privacy risks.
Solution Approach 2:
The patent applies gap introduction locally at changing points rather than globally across the entire trajectory. This localized approach ensures that gaps are only introduced where necessary for privacy protection, preserving data completeness in segments where privacy risk is lower.
Data Source
AI summary
An apparatus and a method for generating sub-trajectories for a trajectory are disclosed. The method includes retrieving trajectory data comprising a plurality of data points defining a trajectory and iteratively executing a set of operations until a termination condition is met. The set of operations include selecting an observation point associated with the plurality of data points and calculating a set of visible points comprising one or more data points from the plurality of data points associated with the observation point based on a predefined visibility context criterion. The method further includes identifying a changing point from the plurality of data points based on the iterative execution of the set of operations, and generating a sub-trajectory for the trajectory such that the changing point indicates a starting point of the sub-trajectory.