Here Global B.v.

Arash Ostadzadeh

Raghavendran Balu

Stefano Bennati

Craig Barnes

Elena Vidyakina

Elena Mumford

Zack Zhu

Aleksandra Kovacevic

Kai Pöthkow

System and methods for augmenting search results with external/user-provided data not contained in a primary datastore. On demand matching is used at run time to match entries in the primary datastore with information in a secondary datastore that is neither indexed nor pre-matched. A location-based query is received. Candidates are generated by searching a primary datastore. A machine learned entity matching network matches the candidates with information in the secondary datastore. The candidates are augmented with the information and returned as search results.

Data augmentation with on demand entity matching

System and methods for augmenting search results with external/user-provided data fields not contained in a primary datastore. A method includes receiving, by a processor, a location-based query; searching, by the processor, a primary datastore for one or more candidates for the location-based query; identifying, by the processor, one or more data fields for the one or more candidates contained in a secondary datastore, the one or more data fields not contained in the primary datastore; augmenting, by the processor, the one or more candidates with the one or more data fields; and providing, by the processor, the augmented one or more candidates.

Data augmentation for location-based searching

Systems and methods for reconstructing a trajectory from anonymized data are provided. In some aspects, a method includes receiving anonymized data corresponding to a trajectory of a user or object, and assembling, based on the anonymized data, a state-space model. The method also includes executing a prediction algorithm, based on the state-space model, to generate predicted data from the anonymized data, and reconstructing the trajectory of the user or object using the predicted data. The method further includes generating a report indicative of the trajectory.

Systems and methods for reconstructing a trajectory from anonymized data

A method, apparatus and computer program product are provided to determine a semantic privacy index that quantifies the risk associated with re-identification of a trajectory following anonymization of the trajectory. In the context of a method, information regarding a trajectory is received. After the trajectory has been map matched to a portion of a road network, the method associates contextual information comprising one or more map features with the trajectory. The method also provides the information regarding the trajectory and the contextual information comprising the one or more map features to a risk estimation model in order to generate the semantic privacy index.

Method, apparatus and computer program product for determining a semantic privacy index

An apparatus for the quantification of privacy risk in geographic data for probe devices in a geographic region includes a database, a sub-trajectory module, a candidate list calculator, and a provision module. The database is configured to store trajectory data based on sequences of sensor measurements. The sub-trajectory module is configured to receive trajectory data points based on sequences of sensor measurements of the probe devices collected in the geographic region and determine sub-trajectories from changes in trajectory identifiers. The candidate list calculator is configured to concatenate at least two of the sub-trajectories based on at least one concatenation parameter. The provision module is configured to calculate a reconstruction rate in the trajectory data and provide the reconstruction rate for a quantification of privacy risk to an external device.

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Nuenen, Netherlands

Arash Ostadzadeh