TrajectoryPoint represents points in time.
Contains a trajectory point, ie. a point in time. Can be iterated over as (timestamp<float>, (latitude<float>, longitude<float>, altitude<float>)). Or one can access the properties timestamp, latitude, longitude or altitude.
geopy.point.Pointinstance representing the location’s latitude, longitude, and altitude.
Implement the two permutation-based methods SwapLocations and ReachLocations described in Anonymization of trajectory data
This method takes reachability constraints into account: from a given location, only those locations at a distance below a threshold following a path in an underlying graph (e.g., urban pattern or road network) are considered to be directly reachable. Enforcing such reachability constraints while requiring full trajectory k-anonymity would result in a lot of original locations being discarded. To avoid this, trajectory k-anonymity is changed by another useful privacy definition: location k-diversity.
Computationally, this means that trajectories are not microaggregated into clusters of size k. Instead, each location is k-anonymized independently using the entire set of locations of all trajectories. To do so, a cluster Cλ of “unswapped” locations is created around a given location λ, i.e. λ ∈ Cλ. The cluster Cλ is constrained as follows:
1) it must have the lowest intra-cluster distance among those clusters of k “unswapped” locations that contain the location λ;
- it must have locations belonging to k different trajectories; and
3) it must contain only locations at a path from λ at most Rs long and with time-stamps differing from tλ at most Rt.
Then, the spatial coordinates (xλ,yλ) are swapped with the spatial coordinates of some random location in Cλ and both locations are marked as “swapped”. If no cluster Cλ can be found, the location λ is removed from the data set and will not be considered anymore in the subsequent anonymization. This process continues until no more “unswapped” locations appear in the data set.
This method needs sets of trajectories as clusters, partitioned using microaggregation. Limit yourself to clustering algorithms which try to minimize the sum of the intra-cluster distances.
The cardinality of each cluster must be approximately k, with k an input parameter, here: cardinality; if the number of trajectories in the cluster is not a multiple of k, one or more clusters must absorb up to k - 1 remaining trajectories, hence those clusters will have cardinalities between k + 1 and 2k − 1. The purpose of setting k as the cluster size is to fulfill trajectory k-anonymity.
The SwapLocations function begins with a random trajectory T in C. The function attempts to cluster each unswapped triple λ in T with another k − 1 unswapped triples belonging to different trajectories such that:
1) the timestamps of these triples differ by no more than a time threshold Rt from the timestamp of λ; and
- the spatial coordinates differ by no more than a space threshold Rs.
If no k − 1 suitable triples can be found that can be clustered with λ, then λ is removed; otherwise, random swaps of triples are performed within the formed cluster. As a result, at least one of the trajectories returned by this function has all its triples swapped.