摘要
根据LBS的特点,为了有效提高隐私保护算法的执行效率,提出了一种基于轨迹相似性的用户LBS双重隐私保护方案。首先当用户发起LBS请求时,分别在用户起始位置和目标位置处的特定区域内生成k-1个随机位置,再随机生成连接起始位置和目标位置的k-1条假轨迹,计算出所有假轨迹和原始轨迹的相似程度,然后选出其中与原始轨迹相似度最低的假轨迹替代用户发起LBS请求;其次,对LBS位置数据集进行预处理,使用字典查询构建位置事务数据库,使用trie树结构存储所得到的位置数据和频率;最后,在trie树上进行频繁位置选取,并使用差分隐私下的拉普拉斯机制扰动位置频率。结果表明,与已有方法相比,对用户的位置隐私进行了双重保护,降低了隐私泄露风险的同时提高了隐私保护算法运算的效率。所提方案在理论分析和实验结果中,可以在安全,隐私和适用性方面取得改进。
According to the characteristics of LBS,in order to effectively improve the effectiveness of the privacy protection algorithm,a user LBS dual privacy protection scheme based on track-like similarity is proposed.Firstly,when a user initiates an LBS request,k-l random positions are generated in a specific area at the user's starting and target positions,and then k-1 false trajectories are randomly generated to connect the starting and target positions.The similarity between all false trajectories and the original trajectory is calculated,and then the false trajectory with the lowest similarity to the original trajectory is selected to replace the user's LBS request;Secondly,preprocess the LBS position data set,using a dictionary query to build a location transaction database,use the TRIE tree structure to store the resulting position data and frequency;Finally,the frequent location is selected on the TRIE tree,and the La-place mechanism disturbance position frequency under differential privacy is used.The results show that compared with the existing method,the user's location privacy has double protection,which reduces the risk of privacy protection algorithm while improving the privacy protection algorithm.The proposed program can be improved in security,priva-cy,and applicability in theoretical analysis and experimental results.
作者
钱坤
李晓会
QIAN Kun;LI Xiao-hui(School of Electronics and Information Engineering,Liaoning University of Technology,Jinzhou Liaoning 121001,China)
出处
《计算机仿真》
北大核心
2023年第11期459-465,共7页
Computer Simulation
基金
国家自然科学基金项目(61802161)
辽宁省教育厅科学研究经费项目(JZL202015402)。
关键词
位置数据
隐私保护
差分隐私
轨迹相似性
Location data
Privacy protection
Differential privacy
Track similarity