Abstract: A method, apparatus and computer program product are provided in order to provide at least approximate real-time intelligent gap placement within mobility data using junctions inferred by features of the mobility data. In this regard, a data chunk associated with a sequence of location probe data points representative of travel of a vehicle along a portion of a road network is received. Additionally, junction behavior in the data chunk is identified based on one or more features for the sequence of location probe data points. Based on a status of a previous data chunk and a junction point that corresponds to a location probe data point immediately after a last probe data point in the junction, a gap placement or a sub-trajectory is applied in the sequence of location probe data points to generate at least a first subsequence and second subsequence of the location probe data points.

Abstract: A method, apparatus and computer program product are provided in order to provide intelligent trajectory configurations within mobility data using junctions inferred by features of the mobility data. In this regard, a sequence of location probe data points representative of travel of a vehicle along a portion of a road network during an interval of time is received. Additionally, junction behavior in the sequence of location probe data points is identified based on one or more features for the sequence of location probe data points. Based on the junction behavior, a last probe data point of a first sub-trajectory for the trajectory and a first probe data point of a second sub-trajectory for the trajectory, a gap placement is applied in the sequence of location probe data points to generate at least a first subsequence of the location probe data points and a second subsequence of the location probe data points.

Abstract: A method, apparatus and computer program product are provided in order to provide intelligent gap placement within mobility data using junctions inferred by features of the mobility data. In this regard, a sequence of location probe data points representative of travel of a vehicle along a portion of a road network during an interval of time is received. Additionally, junction behavior in the sequence of location probe data points is identified based on one or more features for the sequence of location probe data points. Based on a junction point that corresponds to a location probe data point immediately after a last probe data point in the junction, a gap placement in the sequence of location probe data points to generate at least a first subsequence of the location probe data points and a second subsequence of the location probe data points.

Abstract: An approach is provided for data-preserving trajectory anonymization. The approach involves, for example, processing a plurality of location trajectories to determine one or more exchange twists. The plurality of location trajectories are respectively a sequence of locations points determined by a location sensor of a device, and the one or more exchange twists are one or more locations at which at least two trajectories of the plurality of location trajectories match based on a matching criterion. The approach also involves initiating a swapping of one or more trajectory identifiers among the plurality of location trajectories based on the one or more exchange twists to generate anonymized trajectory data. The approach further involves providing the anonymized trajectory data as an output to a location-based service.

Abstract: It is determined whether the number of second probe apparatuses in the vicinity of a first probe apparatus satisfies a volume threshold requirement. Responsive to determining that the volume threshold requirement is satisfied, an instance of individual probe data is generated and provided. Responsive to determining that the volume threshold requirement is not satisfied, it is determined if a first instance of collaborative probe data corresponds to a portion of a trajectory of the first probe apparatus. Responsive to determining that the first instance of collaborative probe data corresponds to the portion of the trajectory, a contribution is added to the first instance of collaborative probe data and the updated first instance of collaborative probe data is provided. Otherwise, a first instance of collaborative probe data is generated that comprises a partial representation of probe data corresponding to the portion of the trajectory of the first probe apparatus.

Abstract: Embodiments described herein relate to anonymizing of trajectories of mobile devices. Methods may include: receiving an input chunk of probe data points, where the input chunk includes probe data points spanning an input duration, where the input chunk represents at least a first input portion of a trajectory; generating, based at least in part on the input chunk, an output chunk of one or more probe data points, where the output chunk includes one or more probe data points spanning an output duration, where the output duration is different from the input duration, where the output chunk represents at least an output portion of the trajectory, where the first input portion of the trajectory and the output portion of the trajectory share at least a common portion of the trajectory; and providing the output chunk of one or more probe data points to a location-based service provider.

Abstract: An approach is provided for probe trajectory anonymization using based on a negative gap. The approach involves, for example, receiving a probe trajectory generated from at least one sensor of a probe device. The approach also involves processing the probe trajectory to segment the probe trajectory into a first subtrajectory and a second subtrajectory based on a negative gap between the first subtrajectory and the second subtrajectory. The negative gap specifies an amount of overlap between the end of the first subtrajectory and the beginning of the second subtrajectory. The approach further involves assigning a first pseudonym (e.g., a first new probe identifier) to the first subtrajectory, and a second pseudonym (e.g., a second new probe identifier) to the second subtrajectory. The approach then involves providing the first subtrajectory and the second subtrajectory as a trajectory anonymization output.

Abstract: Embodiments described herein relate to anonymizing of trajectories of mobile devices through the obfuscation of stay points. Methods may include: receiving probe data points of a trajectory in real-time or near real-time from a probe apparatus as it travels along the trajectory; calculating, for each probe data point, a probability of the trajectory reaching a stay point within a predetermined distance; providing sequential sub-sets of probe data points of the trajectory to a location-based service provider in response to the sequential sub-sets of probe data points including probe data points having a probability failing to satisfy a predetermined value; and identifying a last sub-set of probe data points of the sequential sub-sets of probe data points to provide to the location-based service provider in response to identifying a probe data point having a probability of the trajectory reaching a stay point within the predetermined distance satisfying the predetermined value.

Abstract: An approach is provided for device-side probe trajectory anonymization based on negative gapping. The approach involves, for example, collecting a probe trajectory stream from a sensor of a probe device, wherein the probe trajectory stream comprises a time-sequence of location data points representing a sensed movement of the probe device. The approach also involves generating a plurality of subtrajectory streams from the probe trajectory stream. The approach further involves processing the plurality of subtrajectory streams to create a negative gap between the plurality of subtrajectory streams. The approach further involves providing the plurality of subtrajectory streams as an output in place of the probe trajectory.

Abstract: Embodiments described herein relate to anonymizing of trajectories of mobile devices through the obfuscation of stay points. Methods may include: receiving probe data points of a trajectory in real-time or near real-time from a probe apparatus as it travels along the trajectory; calculating, for each probe data point, a probability of the trajectory reaching a stay point within a predetermined distance; providing sequential sub-sets of probe data points of the trajectory to a location-based service provider in response to the sequential sub-sets of probe data points including probe data points having a probability failing to satisfy a predetermined value; and identifying a last sub-set of probe data points of the sequential sub-sets of probe data points to provide to the location-based service provider in response to identifying a probe data point having a probability of the trajectory reaching a stay point within the predetermined distance satisfying the predetermined value.

Abstract: It is determined whether the number of second probe apparatuses in the vicinity of a first probe apparatus satisfies a volume threshold requirement. Responsive to determining that the volume threshold requirement is satisfied, an instance of individual probe data is generated and provided. Responsive to determining that the volume threshold requirement is not satisfied, it is determined if a first instance of collaborative probe data corresponds to a portion of a trajectory of the first probe apparatus. Responsive to determining that the first instance of collaborative probe data corresponds to the portion of the trajectory, a contribution is added to the first instance of collaborative probe data and the updated first instance of collaborative probe data is provided. Otherwise, a first instance of collaborative probe data is generated that comprises a partial representation of probe data corresponding to the portion of the trajectory of the first probe apparatus.

Abstract: 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.

Abstract: It is determined whether the number of second probe apparatuses in the vicinity of a first probe apparatus satisfies a volume threshold requirement. Responsive to determining that the volume threshold requirement is satisfied, an instance of individual probe data is generated and provided. Responsive to determining that the volume threshold requirement is not satisfied, it is determined if a first instance of collaborative probe data corresponds to a portion of a trajectory of the first probe apparatus. Responsive to determining that the first instance of collaborative probe data corresponds to the portion of the trajectory, a contribution is added to the first instance of collaborative probe data and the updated first instance of collaborative probe data is provided. Otherwise, a first instance of collaborative probe data is generated that comprises a partial representation of probe data corresponding to the portion of the trajectory of the first probe apparatus.

Abstract: An approach is provided for data-preserving trajectory anonymization. The approach involves, for example, processing a plurality of location trajectories to determine one or more exchange twists. The plurality of location trajectories are respectively a sequence of locations points determined by a location sensor of a device, and the one or more exchange twists are one or more locations at which at least two trajectories of the plurality of location trajectories match based on a matching criterion. The approach also involves initiating a swapping of one or more trajectory identifiers among the plurality of location trajectories based on the one or more exchange twists to generate anonymized trajectory data. The approach further involves providing the anonymized trajectory data as an output to a location-based service.

Abstract: An approach is provided for probe trajectory anonymization using based on a negative gap. The approach involves, for example, receiving a probe trajectory generated from at least one sensor of a probe device. The approach also involves processing the probe trajectory to segment the probe trajectory into a first subtrajectory and a second subtrajectory based on a negative gap between the first subtrajectory and the second subtrajectory. The negative gap specifies an amount of overlap between the end of the first subtrajectory and the beginning of the second subtrajectory. The approach further involves assigning a first pseudonym (e.g., a first new probe identifier) to the first subtrajectory, and a second pseudonym (e.g., a second new probe identifier) to the second subtrajectory. The approach then involves providing the first subtrajectory and the second subtrajectory as a trajectory anonymization output.

Abstract: An approach is provided for device-side probe trajectory anonymization based on negative gapping. The approach involves, for example, collecting a probe trajectory stream from a sensor of a probe device, wherein the probe trajectory stream comprises a time-sequence of location data points representing a sensed movement of the probe device. The approach also involves generating a plurality of subtrajectory streams from the probe trajectory stream. The approach further involves processing the plurality of subtrajectory streams to create a negative gap between the plurality of subtrajectory streams. The approach further involves providing the plurality of subtrajectory streams as an output in place of the probe trajectory.

Abstract: 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.

Abstract: It is determined whether the number of second probe apparatuses in the vicinity of a first probe apparatus satisfies a volume threshold requirement. Responsive to determining that the volume threshold requirement is satisfied, an instance of individual probe data is generated and provided. Responsive to determining that the volume threshold requirement is not satisfied, it is determined if a first instance of collaborative probe data corresponds to a portion of a trajectory of the first probe apparatus. Responsive to determining that the first instance of collaborative probe data corresponds to the portion of the trajectory, a contribution is added to the first instance of collaborative probe data and the updated first instance of collaborative probe data is provided. Otherwise, a first instance of collaborative probe data is generated that comprises a partial representation of probe data corresponding to the portion of the trajectory of the first probe apparatus.