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Location Privacy Preservation Methods Based On Differential Privacy

Posted on:2021-04-18Degree:MasterType:Thesis
Country:ChinaCandidate:Q LiuFull Text:PDF
GTID:2428330611990802Subject:Computer Science and Technology
Abstract/Summary:PDF Full Text Request
The geograhic locations of users offered by mobile devices are of great value for crowdsensing applications,such city planning,intelligent transportation system,etc.However,it will leakage users' privacy.The traditional privacy protection models(e.g.,k-anonymity)can not provide sufficient privacy protection because of its vulnerability to background attack.Differential privacy,as a formal framework in statistical database,can quantify individual privacy and resist background attack.We study the protection methods in location privacy based on differential privacy.The main works include:(1)In order to protect the location privacy and encourage users to check in,an incentive mechanism with personalized privacy protection is proposed.First,we perturb the time and location information in the check-in data by using the differential privacy method.We then design a reward strategy based on data quality to increase the quality of check-in data,which calculates the reward amount according to the check-in data quality of check-in users.The simulation experiments indicate that the proposed method can preserve users' privacy effectively,at the same time better guarantee the quality of check-in data.(2)A differentially private spatial decomposition based on Staircase mechanism is proposed.We first investigate the relationship between non-uniform error and query intersection area,and utilize the linear least square to fit the linear relation between them.Then we deduce the optimal partition granularity by minimizing non-uniform error and noise error.In the experiments,we use two real world datasets to evaluate the performance of the proposed method.Experiments show that the proposed two-dimensional spatial publishing method makes a good trade-off between data privacy and utility.(3)In order to improve the performance of trajectory releasing,a trajectory merging publication method based on Staircase mechanism and k-means|| clustering is proposed.wefirst propose two trajectory merging schemes based on k-means|| clustering.Afterwards,we propose a bounded Staircase noise generation algorithm.Theoretical analysis and experimental comparison show that our proposed publication methods significantly outperform existing approaches in terms of data utility and efficiency,while preserving differential privacy.
Keywords/Search Tags:differential privacy, location privacy, location statistic information, trajectory privacy, Staircase mechanism
PDF Full Text Request
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