Key Technologies Of Privacypreserving Query Based On Space-filling Curves For Location-based Services

With the rapid development of intelligent mobile terminals,locationbased services are widely used in various fields such as social life,public safety,epidemic prevention and control,military command,and so on.While location-based services provide convenience to people,it also brings a series of security risks.The service provider can obtain multiple privacy data of users.In addition to location information,the query records of the user also contain sensitive information,such as health status,consumption custom,hobbies,etc.At the same time,more and more enterprises and institutions start to provide location-based services for users by adopting the massive storage capacity and powerful computing capability of cloud computing platform.However,malicious service providers may abuse the original data stored on servers,therefore,the security of the original data in the location-based service is also important for the mode of outsourcing.How to design a reasonable privacy-preserving query scheme to meet the security requirements while ensuring the service quality is a key issue to be solved urgently for location-based services.This paper mainly discusses key problems of privacy-preserving queries for location-based services in the cloud environment,and proposes solutions to these issues.According to the research and analysis of the security requirements for location-based services,this paper utilizes the space-filling curve for data transformation to protect the original data for location-based services in the cloud environment,and proposes three privacy-preserving query schemes for different needs.The proposed schemes solve the key problems of location-based services and improve the theoretical system of privacy-preserving queries for location-based services in the cloud environment.Moreover,the proposed scheme makes a balance among query efficiency,query accuracy and security.The main contributions of this paper are listed as follows:1.This paper designs a privacy-preserving query scheme based on the symmetric-key encryption.The scheme focuses on improving the service quality of privacy-preserving query,that is,providing accurate query results as efficient as possible.The proposed scheme utilizes the dimensionality reduction and clustering characteristics of the space-filling curve to map two-dimensional location information to one-dimensional sequence.It provides a reliable and practical method of data protection for locationbased services in the cloud environment.The proposed scheme can protect the privacy of the original data and the location of the user without the trusted third party,and achieve 100% accurate query.2.This paper introduces a privacy-preserving query scheme based on fully homomorphic encryption.It mainly aims at the security requirements in location-based services,and designs a query scheme that can resist attacks with multiple background knowledge.In order to protect the original data privacy and location privacy of the user,the proposed scheme adopts the space-filling curve to transform the original data.In addition,the protection of query contents for the user is also taken into consideration.Cloud service providers process query requests based on ciphertext calculations.The proposed scheme adopts fully homomorphic encryption to protect query contents of the user,effectively resisting various type of attacks.3.This paper proposes a privacy-preserving query scheme based on an anonymous mechanism,mainly aiming at the balance of the security and effectiveness for privacy-preserving query in location-based services.Considering the personalized privacy needs of users,this paper designs a secure and efficient query scheme that supports the privacy preferences of users.The proposed scheme can protect the original data,user location and query records,while providing an efficient and 100% accurate query.Furthermore,the user can flexibly adjust the trade-off between security and service quality according to their own preferences.