| With the development of Internet technology and the rapid spread of smartphone devices,Location Based Services(LBS)is playing an increasingly important role in people’s daily life.When users enjoy LBS services,they need to submit their personal location information to the server,and how to avoid the risk of privacy leakage through effective methods has become an important research direction for personal privacy and location-based services in the big data environment.Traditional LBS privacy protection methods only protect the privacy of the current location or the current moment,and attackers can use the user’s historical temporal location information to conduct probabilistic speculative attacks(e.g.Bayesian inference attacks),resulting in user privacy leakage.In addition,traditional location privacy protection algorithms ignore the semantic information contained in the location and cannot resist location semantic attacks.To address the above problems of traditional LBS privacy protection,this paper proposes a hidden Markov model-based privacy protection scheme for trajectory publishing based on probabilistic speculative models.Firstly,to portray the Spatio-temporal relationship of the user’s mobile trajectory,this paper uses the Hidden Markov Model(HMM)to model the user’s mobile trajectory and location release and calculates the trajectory release probability vector,through which the probability in the vector is used to falsely release the user’s partial location,to resist Bayesian inference attacks.Secondly,the Sine Cosine Optimisation Algorithm(SCA)and the idea of false location are introduced for optimization,which further improves the operational efficiency and quality of service of the algorithm compared to the Fake Mask algorithm.Finally,the mutual information method is introduced to measure the location semantic similarity,and the semantic set with similarity is used as the location semantic candidate set.Meanwhile,considering the influence of distance on LBS service quality,the Haversine distance formula is used to measure the geographic location offset in the candidate set,and the location point with a small offset degree is selected as the false location to form the false trajectory.In this paper,the proposed scheme is experimentally analyzed using a publicly available dataset.In terms of privacy security,the algorithm in this paper satisfies theγ-privacy requirement,while incorporating a location semantic similarity metric to resist Bayesian inference attacks and location semantic attacks under the forwardbackward algorithm;in terms of service quality,the mathematical expectation of the average trajectory offset degree and the trajectory release probability vector are used as metrics,indicating that this scheme has In terms of quality of service,the average offset of the trajectory and the mathematical expectation of the trajectory release probability vector is used as the metric,indicating that the proposed scheme has the better quality of service.Through experimental analysis,the proposed scheme has further improved the privacy protection effect,quality of service,and the efficiency of finding the release vector compared with other schemes. |
