Research On The Privacy Protection For Location-based Services

With the rapid development of cloud computing and mobile Internet technologies,location-based services have been used more and more,and brought great convenience to people's lives,but at the same time caused serious problems of privacy leakage.To solve this problem,scholars have proposed many algorithms based on location service privacy protection,such as pseudonyms,pseudo-locations,space-time camouflage and space encryption technologies,but whether these privacy protection algorithms can protect people's location privacy or how the level of location privacy protection needs a metric to measure.Most of the existing literatures focus on the construction of location privacy protection algorithm.There are few relatively studies on the measurement of location privacy protection algorithm,and there is obviously a lack of mechanism to measure location privacy protection algorithm.The dissertation analyzes and studies the metric framework of location-based system architecture,attack technology,privacy protection technology and privacy protection.On this basis,two different methods for measuring location privacy protection are proposed:1.A privacy preserving measure method for query under k-anonymity mechanism.The method is based on information entropy and logarithmic function.Initially,a framework for query privacy under the k-anonymity mechanism is established,which contains four roles and four operations,it provides a formal description for privacy measures.Additionally,two quantitative methods of background knowledge are introduced.In second way,because the user attribute discretization values will be calculated as probability expression of background knowledge,the probability is inaccuracy.In the dissertation,the value of each attribute of user after discretization is proposed as the index of the array to calculate the relevant quantities,the array is generated by the relevance of the particular query and the attributes of the user.Then,get the probability that the user issue the particular query.Avoids the influence of discretized values of user attributes on the quantification results.Finally,query privacy measurement is proposed.2.A measure of location privacy disclosure.The measure of location privacy leakage for Greedy-based Location K-anonymous Algorithm(GLKA)is proposed.The method is based on KL scatter(Kullback-Leibler divergence),which integrates the background knowledge of the attacker to measure the leakage of user location privacy in the anonymous area.In the end,the feasibility and effectiveness of the measurement method are verified by experimental methods in the presence or absence of background knowledge,different user privacy requirements and different map grid sizes.Figure[24]table[2]reference[69]...