Aggregating Location Trails for Privacy Preservation
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Solution Overview
Problem
Positional information from computing devices can reveal personally identifiable information, making it difficult to benefit from improved geographic data without compromising user privacy.
Innovation Solution
Aggregating positional information into grids or one-dimensional lines, obfuscating personally identifiable information through resampling, modifying trails, and deleting sensitive location data to minimize identifiable details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If positional information is collected and used to improve geographic information, then geographic data quality is improved, but personally identifiable information becomes derivable from the positional data
Solution Approach 1:
The patent segments positional information into discrete grid cells, where each cell represents an aggregated location bin. Individual device trails are divided into sequences of grid cell visits, transforming continuous positional data into discrete, aggregated units that cannot be easily traced back to specific individuals while preserving geographic movement patterns for map improvement
Solution Approach 2:
The patent merges positional data from multiple computing devices into aggregated trail information. By combining location visits from numerous devices within each grid cell and computing collective statistics (such as visit frequency, duration, and patterns), the system creates aggregated trails that represent collective behavior rather than individual movements, thereby preserving geographic information while eliminating personally identifiable details
2Manufacturing precision
If individual positional trails are aggregated to improve geographic data, then geographic information quality improves, but the aggregation process becomes computationally complex
Solution Approach 1:
The patent segments the aggregation process by dividing geographic space into predetermined grid cells before processing trails. This spatial segmentation allows independent processing of each cell's data, enabling parallel computation and reducing overall complexity. Each grid cell can be processed separately to compute visit statistics, eliminating the need for complex pairwise comparisons across entire trail datasets
Solution Approach 2:
The patent transforms trail data from continuous coordinate representations into discrete grid cell visit sequences. This parameter change from continuous to discrete representation simplifies aggregation operations, as grid cell identifiers can be efficiently counted and statistically analyzed using simple frequency distributions and aggregate metrics rather than complex geometric computations
Data Source
AI summary
Positional information is provided while minimizing the possibility that personally identifiable information can be derived therefrom. Positional information is received in the form of trails that can be aggregated. Individual cells of a grid reflect a quantity of aggregated trails through those cells, an average intensity and direction of movement through those cells, or a more detailed distribution thereof. Alternatively, individual trails are aggregated to an aggregated trail in the form of a line. Further obfuscation of personally identifiable information occurs by resampling aggregated positional information, by introducing false positional information, or by falsely modifying existing positional information, in a manner that does not impact the overall aggregations, and by pruning, or deleting, positional information, especially around sensitive locations, such as a user's home, place of business, or other location that users typically would seek to keep private. Provision of positional information is delayed until a sufficient amount is received.