Approaches For Location Privacy Preserving Based On Feature Security

With the rapid development of the mobile Internet,applications based on location data are emerging one after another,which brings great convenience to people's lives and also poses a huge threat to users' privacy.The location privacy disclosure scenario and the corresponding protection method are the focus of research.The existing research considers the privacy leakage scenario from the perspective of location and trajectory,and the location feature of the location data can also expose the user's private information.Aiming at the problem that location features will expose user privacy,a feature security based location privacy preserving model is proposed.The model divides the location features into release features and disturbance features,defines feature similar communities and feature l-diversity,divides feature similar communities according to the disturbance feature similarity,and then makes the users in the community satisfy the feature l-diversity according to the community partitioning algorithm.On the basis of community division,the feature perturbation algorithm is used to perturb the position data.In view of the existing disturbance method,the relationship between features and the low data availability are not considered.The concept of feature correlation is proposed.The influence of feature correlation is considered in the disturbance process,and the data is guaranteed by narrowing the range of disturbance locations.Finally,a location data set that meets the model requirements is obtained.Based on feature similar community and feature l-diversity,the feature security based location privacy protection method(FSB-LPM)is designed and implemented,including community partitioning algorithm and feature perturbation algorithm,security argument and the algorithm spatiotemporal complexity analysis is given.Data availability is compared on the three general availability indicators and the four custom availability indicators by the random perturbation protection method,the k-anonymity protection method,and two different differential privacy protection methods,on three different data sets.The results together show that FSB-LPM availability is superior to other protection methods in two general availability indicators;among the two custom availability indicators,FSB-LPM is superior to other protection methods.