Privacy Preserving For Spatial Keyword Query

Data outsourcing services can effectively utilize the software and hardware advantages of service providers?for example,cloud computing platforms?to optimize resource allocation.Meanwhile,it can achieve higher maintenance levels and scalability,provide users with centralized and efficient query retrieval services.However,the security issues of service providers can not be ignored.The most important of these is the security protection of outsourced data from data owners and the security of user queries.So far,for the Point-Of-Interest?POI?,such as restaurants,tourist attractions,hotels,etc,researchers have proposed a number of spatial queries based on privacy protection,including:privacy-based skyline query^[12],Privacy-based KNN queries^[17],privacy-protected top-k spatial queries^[22],etc.These studies effectively protect the security of outsourced data.However,these studies are just providing a secure solution for a single data owner.These methods will be difficult to extend an application when a service provider provides spatial queries based on shared data from multiple data owners.We will study the space query algorithm for multi-party data security sharing.The research contents are as follows:Research on index construction methods supporting multi-party spatial data security sharing.It's a security problem that the spatial data structure of data index will expose spatial location and description information of POI.Thus we study the spatial data index structure supporting data privacy protection.Based on the characteristics of high-dimensional spatial data,we studied the high-dimensional security spatial data index structure.Under the premise of ensuring spatial data privacy,it supports spatial query of privacy and security such as Collective Spatial Keyword Querying and Top-K Spatial Keyword Querying.To solve the problem of mutual trust and transaction equality in multi-party data sharing,we studied the collaborative construction mechanism of spatial data security index built by multiple data owners.Research on Collective and Top-k spatial keyword query algorithms supporting privacy protection.Based on the multiplicative homomorphic system,the Top-k spatial keyword query algorithm is studied to realize the privacy-preserving query conditions and query results with the constructed index structure.We analyze the different adversary scenarios of Top-k queries,build attack models,and analyze the security of algorithms.Similarly,the privacy-preserving collective spatial keyword query algorithm is studied to realize the privacy protection for querying user query conditions and query results.