Indexing dynamic encrypted database in cloud for efficient secure k-nearest neighbor query

Secure k-Nearest Neighbor(k-NN)query aims to find k nearest data of a given query from an encrypted database in a cloud server without revealing privacy to the untrusted cloud and has wide applications in many areas,such as privacy-preservingmachine elearning gand secure biometric identification.Several solutions have been put forward to solve this challenging problem.However,the existing schemes still suffer from various limitations in terms of efficiency and flexibility.In this paper,we propose a new encrypt-then-index strategy for the secure k-NN query,which can simultaneously achieve sub-linear search complexity(efficiency)and support dynamical update over the encrypted database(flexibility).Specifically,we propose a novel algorithm to transform the encrypted database and encrypted query points in the cloud.By indexing the transformed database using spatial data structures such as the R-tree index,our strategy enables sub-linear complexity for secure k-NN queries and allows users to dynamically update the encrypted database.To the best of our knowledge,the proposed strategy is the first to simultaneously provide these two properties.Through theoretical analysis and extensive experiments,we formally prove the security and demonstrate the efficiency of our scheme.

Secure K-Nearest neighbor queries in two-tiered mobile wireless sensor networks

Tiered Mobile Wireless Sensor Network(TMWSN)is a new paradigm introduced by mobile edge computing.Now it has received wide attention because of its high scalability,robustness,deployment flexibility,and it has a wide range of application scenarios.In TMWSNs,the storage nodes are the key nodes of the network and are more easily captured and utilized by attackers.Once the storage nodes are captured by the attackers,the data stored on them will be exposed.Moreover,the query process and results will not be trusted any more.This paper mainly studies the secure KNN query technology in TMWSNs,and we propose a secure KNN query algorithm named the Basic Algorithm For Secure KNN Query(BAFSKQ)first,which can protect privacy and verify the integrity of query results.However,this algorithm has a large communication overhead in most cases.In order to solve this problem,we propose an improved algorithm named the Secure KNN Query Algorithm Based on MR-Tree(SEKQAM).The MR-Trees are used to find the K-nearest locations and help to generate a verification set to process the verification of query results.It can be proved that our algorithms can effectively guarantee the privacy of the data stored on the storage nodes and the integrity of the query results.Our experimental results also show that after introducing the MR-Trees in KNN queries on TMWSNs,the communication overhead has an effective reduction compared to BAFSKQ.

A verifiable privacy-preserving data collection scheme supporting multi-party computation in fog-based smart grid

Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the grid.Recently,much attention has been paid to the research on smart grid,especially in protecting privacy and data aggregation.However,most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog computation.In this paper,we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC),named VPDC-MPC,to achieve both functions simultaneously in smart grid based on fog computing.VPDC-MPC realizes verifiable secret sharing of users’data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing scheme.Besides,we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’report.Furthermore,VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis(not only data aggregation)via secure multi-party computation between cloud servers in smart grid.Besides,VPDC-MPC tolerates fault of cloud servers and resists collusion.We also present security analysis and performance evaluation of our scheme,which indicates that even with tradeoff on computation and communication overhead,VPDC-MPC is practical with above features.

Search geometric ranges efficiently as keywords over encrypted spatial data

With the increasing popularity of location-based services(LBS),data outsourcing toward clouds is an emerging paradigm for ease of data management by LBS providers.Geometric range queries are one of the fundamental search functions in LBS,which are to find points inside geometric areas(e.g.,circles or polygons).To ensure data confidentiality,the service users tend to encrypt the data before outsourcing it.However,regarding encrypted data,only a few consider geometric range queries,where the rationale is the high-dimension calculations make these queries particularly harder.In this paper,we propose a novel scheme for geometric range queries,that can provide the privacy of data stored at a cloud server and queries.Our scheme supports querying encrypted spatial data with irregular-shaped areas,achieves fast searches and enables dynamic updates.Experimental results over real-world spatial datasets demonstrate that our scheme results in fewer communication rounds and can speed up the search time 4×compared to state-of-the-art schemes,without carrying any potentially visible leakage in the structure.