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: 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: A method, apparatus, and computer program product are provided for anonymizing the trajectory of a vehicle. Methods may include: receiving a sequence of probe data points defining a trajectory; for a subset of the sequence of probe data points defining the trajectory beginning at an origin: updating a counter value at each probe data point, where the counter value is updated based, at least in part, on properties of a number of road links emanating from each junction through which the trajectory passed to reach a location associated with the respective probe data point; in response to the counter value satisfying a predetermined value after an update relative to a given probe data point, removing probe data points before the given probe data point in the sequence of probe data points to obtain origin-obscured probe data points; and creating a cropped trajectory including the origin-obscured probe data points.

Abstract: Embodiments described herein relate to anonymizing of trajectories of mobile devices through the introduction of gaps between sub-trajectories. Methods may include: receiving a set of probe data points defining a trajectory; identifying a temporal length range of sub-trajectories; receiving a mode, where the mode is established based on a preceding set of probe data points defining a trajectory, where the mode includes an indication of whether to generate a sub-trajectory or a gap from the beginning of the received set of probe data points; and establishing at least one sub-trajectory including a sub-set of the set of probe data points, where the at least one sub-trajectory is established to satisfy the temporal length range of sub-trajectories.

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: 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: 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: Embodiments described herein relate to anonymizing of trajectories of mobile devices through the introduction of gaps between sub-trajectories. Methods may include: receiving a set of probe data points defining a trajectory; identifying a temporal length range of sub-trajectories; receiving a mode, where the mode is established based on a preceding set of probe data points defining a trajectory, where the mode includes an indication of whether to generate a sub-trajectory or a gap from the beginning of the received set of probe data points; and establishing at least one sub-trajectory including a sub-set of the set of probe data points, where the at least one sub-trajectory is established to satisfy the temporal length range of sub-trajectories.

Abstract: An approach is disclosed for a data-driven evaluation of heuristics for trajectory cropping. The approach involves, e.g., determining a cropping heuristic, wherein the cropping heuristic comprises an algorithm for cropping a probe trajectory collected from one or more sensors of a mobile device to anonymize the probe trajectory data. The approach also involves processing the probe trajectory using the cropping heuristic to generate a cropped probe trajectory. The approach further involves extracting one or more heuristic-based features of the cropped probe trajectory data. The approach also involves extracting one or more privacy-based features from a privacy preference, wherein the one or more privacy-based features represent a target level of cropping to meet the privacy preference. The approach further involves computing a score based on a distance between the one or more heuristic-based features and the one or more privacy-based features. The approach further involves providing the score as an output.

Abstract: A method, apparatus, and computer program product are provided for anonymizing the trajectory of a vehicle. Methods may include: receiving a sequence of probe data points defining a trajectory; for a subset of the sequence of probe data points defining the trajectory beginning at an origin: updating a counter value at each probe data point, where the counter value is updated based, at least in part, on properties of a number of road links emanating from each junction through which the trajectory passed to reach a location associated with the respective probe data point; in response to the counter value satisfying a predetermined value after an update relative to a given probe data point, removing probe data points before the given probe data point in the sequence of probe data points to obtain origin-obscured probe data points; and creating a cropped trajectory including the origin-obscured probe data points.

Abstract: A method, apparatus, and computer program product are provided for anonymizing the trajectory of a vehicle. Methods may include: receiving a sequence of probe data points defining a trajectory; for a subset of the sequence of probe data points defining the trajectory beginning at an origin: updating a counter value at each probe data point, where the counter value is updated based, at least in part, on properties of a number of road links emanating from each junction through which the trajectory passed to reach a location associated with the respective probe data point; in response to the counter value satisfying a predetermined value after an update relative to a given probe data point, removing probe data points before the given probe data point in the sequence of probe data points to obtain origin-obscured probe data points; and creating a cropped trajectory including the origin-obscured 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: 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 for estimating parking availability in an area based on probe data points in response to a request while maintaining data privacy. A method may include: receiving probe data associated with a plurality of vehicles; determining, from the probe data, location information, parking event information, and a timestamp; receiving a parking availability query, where the parking availability query includes a location; generating and transmitting a request for parking availability information corresponding to the location; receiving a plurality of partial results in response to the request for parking availability information corresponding to the location; determining, from the plurality of partial results, parking availability information corresponding to the location; and providing the parking availability information corresponding to the location responsive to the parking availability query.

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.