基于多边缘服务器的个性化搜索隐私保护方法

Method of privacy protection based on multiple edge servers in personalized search

在明文环境下根据用户的兴趣模型以及查询关键词能够获得用户个性化的搜索结果会导致敏感数据和用户隐私信息的泄露,不利于含有敏感数据的云搜索服务的推广,鉴于此,数据通常以密文的形式存储在云服务器中。用户在使用云搜索服务时,希望在海量的密文中快速地获得自己想要的搜索结果。为了解决这一问题,在个性化搜索中提出了一种基于多边缘服务器的隐私保护方法,该方法通过引入多个边缘服务器,并通过切割索引与查询矩阵,实现了在边缘服务器上计算部分用户查询与部分文件索引之间的相关性得分,云服务器只需要将边缘服务器上得到的相关性得分做简单处理即能返回与用户查询最相关的前K个文件,使其特别适用于大量用户在海量密文中的个性化搜索。安全分析和实验结果表明,该方法能很好地保护用户的隐私以及数据的机密性,并具有高效的搜索效率,能为用户提供了更好的个性化搜索体验。

In the plaintext environment,users'personalized search results can be obtained through users'interest model and query keywords.However,it may possibly result in the disclosure of sensitive data and privacy,which prevents using sensitive data in cloud search.Therefore,data is generally stored in the form of ciphertext in the cloud server.In the process of cloud search service,users intend to quickly obtain the desired search results from the vast amount of ciphertext.In order to solve the problem,it was proposed that a method of privacy protection based on multiple edge servers in personalized search shall be used.By introducing multiple edge servers and cutting the index as well as the query matrix,the computing relevance scores of partial query and partial file index are achieved on the edge server.The cloud server only needs to get the relevance score on the edge server and make a simple processing that can return to the most relevant Top K files by user query,so as to make it particularly suitable for a large number of users in the massive personalized ciphertext search.Security analysis and experimental results show that this method can effectively protect users’privacy and data confidentiality.In addition,it can guarantee high efficiency in search to provide better personalized search experience.