Trajectory Data Releasing Method Via Differential Privacy Based On Spatial Partition

A number of security and privacy challenges of cyber system are arising due to the rapidly evolving scale and complexity of modern system and networks.The cyber system is a fundamental ingredient for Internet of Things(IoT)and smart city which are driven by huge amount of data.There data are generated by users and collected by service provider or certain applications,which are published after data mining and statistic analysis with results that can benefit users and service provider.However,when users are enjoying the convenience from data analysis,there come personal privacy and security issues.That is because these data carry a lot of personal information such as location,fingerprints,or face images etc.,for mining and analysis.For example,attacker can easily get the relationship between user and his trajectory according to background knowledge and statistic query based on unprotected publishing location or trajectory.If unprotected trajectory data is released,it may disclose user's personal privacy,such as home,religion,behavior mode,etc.,which will endanger their personal security.Until now,many methods for protecting trajectory information have been proposed,a lot of them are k-anonymity based.However,these methods have the following deficiencies:(1)it cannot defend against speculative attacks if the attacker's background knowledge is maximized;(2)when studying the problem,they made some strong assumptions that did not match the reality;(3)the implementation algorithm is complicated and the time complexity is high,which means that data cannot be executed quickly when the amount is large.So,in this paper we propose a spatial partition based method to publish trajectory data via differential privacy.First,by exponential mechanism we divide location set at the same time into different partitions fast and accurately.Then we propose another effective method to release trajectories in a differential private manner.We design experiment based on the real-life dataset and compare it with existing method.The results show that the trajectory dataset released by our algorithm has better usability while ensuring privacy.