Triangulation for k-anonymity in location trajectory data

a technology of location trajectory and triangulation, applied in relational databases, database models, instruments, etc., can solve problems such as unquestionable use of such publishing, unintended consequences, harm to the safety and security of contributors

Active Publication Date: 2020-01-16
HERE GLOBAL BV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Though the usefulness of such publishing is unquestionable, the trade-off being made is privacy of contributing users and control over data.
When it is possible to trace back the contributing user it might lead to unintended consequences that harms the safety and security of contributors.
A data breach by any of these applications may result in the identity and habits of the users being compromised.
In practice, this does little to mitigate the risks because an adversary may break the protection and identify the user with external information.
A repeatedly observed trip from a residential place to a business place may suggest the home location and office location; frequent visits to a hospital may suggest health issues.
In another aspect, strong privacy preserving techniques involving data perturbation, lead to noise data that is published impacting the utility of shared data.
K-anonymization and clustering methods have a strong connection, as the underlying problem is determining an optimal grouping that best represents the structure of the data.
Releasing or publishing an unchanged version of the sequence of spatiotemporal coordinates may violate usual privacy (e.g., reveal t

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  • Triangulation for k-anonymity in location trajectory data
  • Triangulation for k-anonymity in location trajectory data
  • Triangulation for k-anonymity in location trajectory data

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first embodiment

[0063]The anonymity controller 121 is configured to cluster the trajectory data. Throughout this discussion the manipulation of the trajectory data may be done on the point level (e.g., sampled points 51) or on the sequences of sampled points, which are referred to as trajectories. The anonymity controller 121 executes a clustering technique such as K-means clustering with the constraint that each cluster has at least K members. The clustering technique may also include modifications to suit the special requirements of spatial clustering. The modifications may be spatial properties, density variation, or other considerations. The characterization and clustering phase of the first embodiment is not tuned optimally to underlying data and is sensitive to user parameters. The density variation is minimized with minimal user input.

[0064]The K-means clustering, or another type of initial clustering, may include an initial clustering technique. The anonymity controller 121 is configured to...

embodiment 1

[0209]The following example embodiments of the invention are also disclosed:[0210] A method for providing anonymity in geographic data for probe devices in a geographic region for a location-based service, the method comprising:

[0211]receiving trajectory data based on sequences of sensor measurements of the probe devices;

[0212]calculating, by a processor, a plurality of triangles from the trajectory data, wherein each of the plurality of triangles is defined by vertices;

[0213]calculating, by the processor, a similarity score for the vertices based on trajectory data associated with adjacent vertices;

[0214]modifying at least one vertex in response to the similarity score;

[0215]calculating, by the processor, a centrality score for each of the plurality of triangles in response to the at least one vertex;

[0216]assigning geographic locations to the trajectory data according to the centrality score; and

[0217]providing the geographic location to the location-based service.[0218]Embodiment...

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Abstract

An apparatus for providing anonymity in geographic data for probe devices in a geographic region for a location-based service includes at least a database and a triangulation calculator. The database is configured to store trajectory data based on sequences of sensor measurements of the probe devices. The triangulation calculator is configured to calculate triangles from the trajectory data. Each of the triangles is defined by vertices. The triangulation calculator is configured to calculate a similarity score for the vertices based on trajectory data associated with adjacent vertices. At least one vertex is modified in response to the similarity score.

Description

FIELD[0001]The following disclosure relates to anonymity for probe data for location-based services.BACKGROUND[0002]The Global Positioning System (GPS) or another global navigation satellite system (GNSS) provides location information to a receiving device anywhere on Earth as long as the device has a substantial line of sight without significant obstruction to three or four satellites of the system. Location-based services control features of an application based on location information from a GNSS or another source.[0003]The increasing trend of smart phones and wide spread integration of GPS devices in vehicles lead to availability of large pool of user location data including stay-points, checkins and mobility traces. When such mobility data is aggregated in a centralized manner, it makes new applications such as traffic analysis and prediction possible. The aggregated and sharing of mobility traces data is called trajectory data publishing.[0004]Trajectory data publishing is cen...

Claims

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Application Information

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IPC IPC(8): G06F17/11G06F17/30H04W4/02H04W4/38G06F21/62
CPCG06F17/11H04W4/38G06F16/29G06F21/6218H04W4/025G06F16/287G06F16/909G06F21/6254H04W4/024H04W4/029H04W4/80
Inventor BALU, RAGHAVENDRAN
Owner HERE GLOBAL BV
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