Research On Location Privacy Preserving Query Techniques In Road Network

With the popularity of location services and the development of network,users pay more attention to location privacy.The most common location service is nearest neighbor query.The nearest neighbor query service in road network becomes a hot spot for location privacy-preserving technology because of its correlation of real life.In view of the security risks caused by the trusted anonymous server for the existing research,Privacy-protected methods without trusted third party are proposed in both snapshot query and continuous query.The thesis work is as follows:(1)A privacy-preserving snapshot query based on partial index mechanism is proposed to solve the low utilization rate of server-side global road network index.The client generates an anonymous sequence where the road segment that user locates in meets l-diversity,and communicates with LBS server several rounds to query the k nearest neighbors without location leakage.At the server,the whole road network is partitioned into a series of basic units.A hitting frequency guided segmented query processing strategy is proposed,which differentiates basic units with high hitting frequency from those ones with low hitting frequency.This mechanism reduce index storage cost originated from global server side index structure.(2)A privacy-preserving continuous query based on regional dynamic index is raised to avoid security risks of anonymous server and high cost of query in server.Client communicates with LBS server directly and generates consecutive cloaking regions to resist location-based attacks,and determines the query time based on user's movement direction.Meanwhile,the consecutive cloaking regions intersect in small area to avoid the plentiful repetitive queries on server side.At the server,the DNRT structure based on spatial partition is designed to store the neighbors of road nodes queried frequently and reduce computation cost on server-side,and the bit stream is used to reduce communication cost.Theoretical analysis and experimental results show the proposed method can provide accurate and efficient knearest neighbor query service while satisfying user's privacy requirements.