Shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means - Suxia ZHU; Shulun LIU; Guanglu SUN (Tongxin xuebao)
To solve the problem that most studies had not fully considered the sensitivity of location to privacy budget and the influence of trajectory shape, which made the usability of published trajectory poor, a shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means was proposed.Firstly, according to the topological relationship of geographic space, relative entropy was used to calculate the sensitivity of real location to privacy budget, a real-time calculation method of location sensitive privacy level was designed, and a new privacy model was built in combination with differential privacy budget.Secondly, K-means algorithm was used to cluster the release position to obtain the release position set that was most similar to the real position direction, and Fréchet distance was introduced to measure the similarity between the release track and the real track, so as to improve the availability of the release track.Experiments on real data sets show that the proposed trajectory protection mechanism has obvious advantages in trajectory availability compared with others.