A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cell...A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cells)could become the anchor of linkage attacks to re-identify the users.Focusing on trajectory privacy in online health monitoring,we propose the User Trajectory Model(UTM),a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data(e.g.,number of individuals covered by each small cell),and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness(i.e.,the expectation and the variance).Eventually,exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation,confirm our theoretical deductions and present counter-intuitive insights.展开更多
Location-based services provide service and convenience,while causing the leakage of track privacy.The existing trajectory privacy protection methods lack the consideration of the correlation between the noise sequenc...Location-based services provide service and convenience,while causing the leakage of track privacy.The existing trajectory privacy protection methods lack the consideration of the correlation between the noise sequence,the user’s original trajectory sequence,and the published trajectory sequence.And they are susceptible to noise filtering attacks using filtering methods.In view of this problem,a differential privacy trajectory protection method based on spatiotemporal correlation is proposed in this paper.With this method,the concept of correlation function was introduced to establish the correlation constraint of release track sequence,and the least square method was used to fit the user’s original track and the overall direction of noise sequence to construct noise candidate set.It ensured that the added noise sequence has spatiotemporal correlation with the user’s original track sequence and release track sequence.Also,it effectively resists attackers’denoising attacks,and reduces the risk of trajectory privacy leakage.Finally,comparative experiments were carried out on the real data sets.The experimental results show that this method effectively improves the privacy protection effect and the data availability of the release track,and it also has better practicability.展开更多
To solve the privacy leakage problem of truck trajectories in intelligent logistics,this paper proposes a quadtreebased personalized joint location perturbation(QPJLP)algorithm using location generalization and local ...To solve the privacy leakage problem of truck trajectories in intelligent logistics,this paper proposes a quadtreebased personalized joint location perturbation(QPJLP)algorithm using location generalization and local differential privacy(LDP)techniques.Firstly,a flexible position encoding mechanism based on the spatial quadtree indexing is designed,and the length of the encoding can be adjusted freely according to data availability.Secondly,to meet the privacy needs of different locations of users,location categories are introduced to classify locations as sensitive and ordinary locations.Finally,the truck invokes the corresponding mechanism in the QPJLP algorithm to locally perturb the code according to the location category,allowing the protection of non-sensitive locations to be reduced without weakening the protection of sensitive locations,thereby improving data availability.Simulation experiments demonstrate that the proposed algorithm effectively meets the personalized trajectory privacy requirements while also exhibiting good performance in trajectory proportion estimation and top-k classification.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61871062and Grant 61771082the Natural Science Foundation of Chongqing of China under Grant cstc2013jcyjA40066+3 种基金the Program for Innovation Team Building at Institutions of Higher Education in Chongqing under Grant CXTDX201601020the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN201801316the Key Industrial Technology Development Project of Chongqing of China Development and Reform Commission under Grant 2018148208the Innovation and Entrepreneurship Demonstration Team of Yingcai Program of Chongqing of China under Grant CQYC201903167.
文摘A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cells)could become the anchor of linkage attacks to re-identify the users.Focusing on trajectory privacy in online health monitoring,we propose the User Trajectory Model(UTM),a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data(e.g.,number of individuals covered by each small cell),and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness(i.e.,the expectation and the variance).Eventually,exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation,confirm our theoretical deductions and present counter-intuitive insights.
文摘Location-based services provide service and convenience,while causing the leakage of track privacy.The existing trajectory privacy protection methods lack the consideration of the correlation between the noise sequence,the user’s original trajectory sequence,and the published trajectory sequence.And they are susceptible to noise filtering attacks using filtering methods.In view of this problem,a differential privacy trajectory protection method based on spatiotemporal correlation is proposed in this paper.With this method,the concept of correlation function was introduced to establish the correlation constraint of release track sequence,and the least square method was used to fit the user’s original track and the overall direction of noise sequence to construct noise candidate set.It ensured that the added noise sequence has spatiotemporal correlation with the user’s original track sequence and release track sequence.Also,it effectively resists attackers’denoising attacks,and reduces the risk of trajectory privacy leakage.Finally,comparative experiments were carried out on the real data sets.The experimental results show that this method effectively improves the privacy protection effect and the data availability of the release track,and it also has better practicability.
基金Key Scientific Research Projects of Colleges and Universities in Henan Province(23A520033)Doctoral Scientific Fund of Henan Polytechnic University(B2022-16).
文摘To solve the privacy leakage problem of truck trajectories in intelligent logistics,this paper proposes a quadtreebased personalized joint location perturbation(QPJLP)algorithm using location generalization and local differential privacy(LDP)techniques.Firstly,a flexible position encoding mechanism based on the spatial quadtree indexing is designed,and the length of the encoding can be adjusted freely according to data availability.Secondly,to meet the privacy needs of different locations of users,location categories are introduced to classify locations as sensitive and ordinary locations.Finally,the truck invokes the corresponding mechanism in the QPJLP algorithm to locally perturb the code according to the location category,allowing the protection of non-sensitive locations to be reduced without weakening the protection of sensitive locations,thereby improving data availability.Simulation experiments demonstrate that the proposed algorithm effectively meets the personalized trajectory privacy requirements while also exhibiting good performance in trajectory proportion estimation and top-k classification.
