Research On Privacy Anonymity Algorithms Based On Clustering

In recent years,as the rapid development of information technology,mobile communication technology,location-aware technology and Internet technology,all the field have produced a vast user data,especially the personal position and trajectory data.Analyzing and mining data can help us obtain potential rules and commercial value.However,the data is double-edged sword,as long as there is data,there must be security and privacy issues.In order to analyze the data,it is often necessary to release data stored on the server,and it is no doubt that the malicious user(attacker)will take advantage of this opportunity to threaten users' privacy.Speculative attack against the user information may lead to the leak of personal interests,behavior patterns,social habits and other private information,and threaten individual's life and property safety of users,seriously.Therefore,in order to protect the personal privacy of released data,we need to research the techniques and methods of data releasing,to protect the privacy of released data,as well as maintain high data utility.In this research,we use two typical data type of the privacy protection field---relational data and trajectory data as the research background,and study privacy anonymous methods.In the premise of ensuring the data security,through corresponding anonymous technology,our research losses appropriate information to exchange the higher availability of anonymous data.Eventually,it achieves the purpose of balancing the data availability and privacy protection degree.Specifially,the main contents of the paper can be divided into four parts:Firstly,while achieving k-anonymity,lots of existing algorithms based on limited release use generalization technology based on divide and conquer strategy,and reduce potential number of anonymous groups;Although the partition strategy based on rounded partition function avoid the situation that it is likely to reduce the number of potential anonymous groups,it does not consider the distance of adjacent data point in the temporary anonymous group,and can easily produce much unnecessary information loss during the division process,and affect the availability of released anonymous data sets.Meanwhile,in the p-sensitive kanonymity model,the uneven distribution of the sensitive attribute values in the clustering results may cause sensitive information disclosure.Therefore,we propose the algorithm for k-anonymity based on projection area density partition and a micro-aggregation algorithm based on sensitive attribute entropy,to solve these problems of k-anonymity,from the two aspects of data availability and privacy,respectively.Secondly,in the trajectory data,the biggest privacy threat is the "sensitive position leakage",If the attacker is able to understand the position and time that someone visits at,the attacker will be able to determine the person's true record in the publication database,and understand the person's other trajectory information,then get the person's interests,hobbies,behavior patterns,social customs and other private information,and result in the leakage of the person's privacy information.Therefore,we present a trajectory similarity measure model and a privacy preserving algorithm based on trajectory location and shape similarity,which maximizes the trajectory similarity in the clusters,and forms data "mask" which is formed by fully accurate true original locations information to meet the trajectory k-anonymity.While protecting trajectory data,the algorithm effectively improves the availability of trajectory data.Thirdly,in real applications,different mobile users' privacy need are different,for example: some users will see his address as privacy,and some one will not think so.Simply seeing the level of privacy protection for all mobile users as equivalent is unreasonable.Meeting moving objects' personalized privacy needs will not only improve the privacy protection level of moving objects,but also effectively reduce unnecessary information loss,during the anonymous process.Meanwhile,the existing trajectory anonymous algorithms are still not fully consider theinternal and external trajectory feature information,while calculating the trajectory similarity.To solve these problems,we propose the concept of personalized trajectory k-anonymity and trajectory structure similarity measure model,and propose the sparse minimum spanning tree clustering based personalized trajectory privacy protection algorithm.It generates an approximate optimal trajectory k-anonymity set by greedy strategy,and increases the availability of trajectory data significantly.Finally,while calculating trajectory similarity,existing trajectory anonymous algorithm only consider the location proximity of sample point,and it belongs to static proximity research of moving object.During the process of forming tajectory k-anonymous set of moving object,we often encounter such a situation: as the objects moving,the distance of the adjacent objects at the start time will be increasing,and the distance of the objects which are far away at the start time will be decreasing.Therefore,we present the concept of neighborhood similarity and neighborhood distortion density to fully consider the dynamics proximity of locations in the trajectory,and then propose two algorithms-Trajectory Anonymity Algorithm based on Neighborhood Similarity and Trajectory Anonymity Algorithm based on Trajectory Neighborhood Distortion Density to solve the problem.