With the continuous growth of exponential data in IoT,it is usually chosen to outsource data to the cloud server.However,cloud servers are usually provided by third parties,and there is a risk of privacy leakage.Encry...With the continuous growth of exponential data in IoT,it is usually chosen to outsource data to the cloud server.However,cloud servers are usually provided by third parties,and there is a risk of privacy leakage.Encrypting data can ensure its security,but at the same time,it loses the retrieval function of IoT data.Searchable Encryption(SE)can achieve direct retrieval based on ciphertext data.The traditional searchable encryption scheme has the problems of imperfect function,low retrieval efficiency,inaccurate retrieval results,and centralized cloud servers being vulnerable and untrustworthy.This paper proposes an Efficient searchable encryption scheme supporting fuzzy multi-keyword ranking search on the blockchain.The blockchain and IPFS are used to store the index and encrypted files in a distributed manner respectively.The tamper resistance of the distributed ledger ensures the authenticity of the data.The data retrieval work is performed by the smart contract to ensure the reliability of the data retrieval.The Local Sensitive Hash(LSH)function is combined with the Bloom Filter(BF)to realize the fuzzy multi-keyword retrieval function.In addition,to measure the correlation between keywords and files,a new weighted statistical algorithm combining RegionalWeight Score(RWS)and Term Frequency–Inverse Document Frequency(TF-IDF)is proposed to rank the search results.The balanced binary tree is introduced to establish the index structure,and the index binary tree traversal strategy suitable for this scheme is constructed to optimize the index structure and improve the retrieval efficiency.The experimental results show that the scheme is safe and effective in practical applications.展开更多
The paper presents a novel benefit based query processing strategy for efficient query routing. Based on DHT as the overlay network, it first applies Nash equilibrium to construct the optimal peer group based on the c...The paper presents a novel benefit based query processing strategy for efficient query routing. Based on DHT as the overlay network, it first applies Nash equilibrium to construct the optimal peer group based on the correlations of keywords and coverage and overlap of the peers to decrease the time cost, and then presents a two-layered architecture for query processing that utilizes Bloom filter as compact representation to reduce the bandwidth consumption. Extensive experiments conducted on a real world dataset have demonstrated that our approach obviously decreases the processing time, while improves the precision and recall as well.展开更多
Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage.However,the current searchable encryption solutions still have deficiencies in search efficiency...Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage.However,the current searchable encryption solutions still have deficiencies in search efficiency,accuracy and multiple data owner support.In this paper,we propose an encrypted data searching scheme that can support multiple keywords fuzzy search with order preserving(PMS).First,a new spelling correction algorithm-(Possibility-Levenshtein based Spelling Correction)is proposed to correct user input errors,so that fuzzy keywords input can be supported.Second,Paillier encryption is introduced to calculate encrypted relevance score of multiple keywords for order preserving.Then,a queue-based query method is also applied in this scheme to break the linkability between the query keywords and search results and protect the access pattern.Our proposed scheme achieves fuzzy matching without expanding the index table or sacrificing computational efficiency.The theoretical analysis and experiment results show that our scheme is secure,accurate,error-tolerant and very efficient.展开更多
In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on...In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.展开更多
Attribute-based encryption is cryptographic techniques that provide flexible data access control to encrypted data content in cloud storage.Each trusted authority needs proper management and distribution of secret key...Attribute-based encryption is cryptographic techniques that provide flexible data access control to encrypted data content in cloud storage.Each trusted authority needs proper management and distribution of secret keys to the user’s to only authorized user’s attributes.However existing schemes cannot be applied multiple authority that supports only a single keywords search compare to multi keywords search high computational burden or inefficient attribute’s revocation.In this paper,a ciphertext policy attribute-based encryption(CP-ABE)scheme has been proposed which focuses on multi-keyword search and attribute revocation by new policy updating feathers under multiple authorities and central authority.The data owner encrypts the keywords index under the initial access policy.Moreover,this paper addresses further issues such as data access,search policy,and confidentiality against unauthorized users.Finally,we provide the correctness analysis,performance analysis and security proof for chosen keywords attack and search trapdoor in general group model using DBDH and DLIN assumption.展开更多
With the rapid growth of spatial data,POI(Point of Interest)is becoming ever more intensive,and the text description of each spatial point is also gradually increasing.The traditional query method can only address the...With the rapid growth of spatial data,POI(Point of Interest)is becoming ever more intensive,and the text description of each spatial point is also gradually increasing.The traditional query method can only address the problem that the text description is less and single keyword query.In view of this situation,the paper proposes an approximate matching algorithm to support spatial multi-keyword.The fuzzy matching algorithm is integrated into this algorithm,which not only supports multiple POI queries,but also supports fault tolerance of the query keywords.The simulation results demonstrate that the proposed algorithm can improve the accuracy and efficiency of query.展开更多
Data outsourcing has become an industry trend with the popularity of cloud computing.How to search data securely and efficiently has received unprecedented attention.Dynamic Searchable Symmetric Encryption(DSSE)is an ...Data outsourcing has become an industry trend with the popularity of cloud computing.How to search data securely and efficiently has received unprecedented attention.Dynamic Searchable Symmetric Encryption(DSSE)is an effective method to solve this problem,which supports file updates and keyword-based searches over encrypted data.Unfortunately,most existing DSSE schemes have privacy leakages during the addition and deletion phases,thus proposing the concepts of forward and backward privacy.At present,some secure DSSE schemes with forward and backward privacy have been proposed,but most of these DSSE schemes only achieve single-keyword query in the single-client setting,which seriously limits the application in practice.To solve this problem,we propose a multi-client and multikeyword searchable symmetric encryption scheme with forward and backward privacy(MMKFB).Our scheme focuses on the multi-keyword threshold queries in the multi-client setting,which is a new pattern of multi-keyword search realized with the help of additive homomorphism.And performance analysis and experiments demonstrate that our scheme is more practical for use in small and medium size databases.Especially when a large number of files are updated at once,our scheme has advantages over some existing DSSE schemes in terms of computational efficiency and client storage overhead.展开更多
To achieve the confidentiality and retrievability of outsourced data simultaneously,a dynamic multi-keyword fuzzy ranked search scheme(DMFRS)with leakage resilience over encrypted cloud data based on two-level index s...To achieve the confidentiality and retrievability of outsourced data simultaneously,a dynamic multi-keyword fuzzy ranked search scheme(DMFRS)with leakage resilience over encrypted cloud data based on two-level index structure was proposed.The first level index adopts inverted index and orthogonal list,combined with 2-gram and location-sensitive Hashing(LSH)to realize a fuzzy match.The second level index achieves user search permission decision and search result ranking by combining coordinate matching with term frequency-inverse document frequency(TF-IDF).A verification token is generated within the results to verify the search results,which prevents the potential malicious tampering by cloud service providers(CSP).The semantic security of DMFRS is proved by the defined leakage function,and the performance is evaluated based on simulation experiments.The analysis results demonstrate that DMFRS gains certain advantages in security and performance against similar schemes,and it meets the needs of storage and privacy-preserving for outsourcing sensitive data.展开更多
基金funded by the Jilin Provincial Department of Education Scientific Research Project(Project No.JJKH20250872KJ).
文摘With the continuous growth of exponential data in IoT,it is usually chosen to outsource data to the cloud server.However,cloud servers are usually provided by third parties,and there is a risk of privacy leakage.Encrypting data can ensure its security,but at the same time,it loses the retrieval function of IoT data.Searchable Encryption(SE)can achieve direct retrieval based on ciphertext data.The traditional searchable encryption scheme has the problems of imperfect function,low retrieval efficiency,inaccurate retrieval results,and centralized cloud servers being vulnerable and untrustworthy.This paper proposes an Efficient searchable encryption scheme supporting fuzzy multi-keyword ranking search on the blockchain.The blockchain and IPFS are used to store the index and encrypted files in a distributed manner respectively.The tamper resistance of the distributed ledger ensures the authenticity of the data.The data retrieval work is performed by the smart contract to ensure the reliability of the data retrieval.The Local Sensitive Hash(LSH)function is combined with the Bloom Filter(BF)to realize the fuzzy multi-keyword retrieval function.In addition,to measure the correlation between keywords and files,a new weighted statistical algorithm combining RegionalWeight Score(RWS)and Term Frequency–Inverse Document Frequency(TF-IDF)is proposed to rank the search results.The balanced binary tree is introduced to establish the index structure,and the index binary tree traversal strategy suitable for this scheme is constructed to optimize the index structure and improve the retrieval efficiency.The experimental results show that the scheme is safe and effective in practical applications.
基金Supported by the National Natural Science Foundation of China (60673139, 60473073, 60573090)
文摘The paper presents a novel benefit based query processing strategy for efficient query routing. Based on DHT as the overlay network, it first applies Nash equilibrium to construct the optimal peer group based on the correlations of keywords and coverage and overlap of the peers to decrease the time cost, and then presents a two-layered architecture for query processing that utilizes Bloom filter as compact representation to reduce the bandwidth consumption. Extensive experiments conducted on a real world dataset have demonstrated that our approach obviously decreases the processing time, while improves the precision and recall as well.
基金This work is supported by the National Natural Science Foundation of China under Grant 61402160 and 61872134Hunan Provincial Natural Science Foundation under Grant 2016JJ3043Open Funding for Universities in Hunan Province under grant 14K023.
文摘Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage.However,the current searchable encryption solutions still have deficiencies in search efficiency,accuracy and multiple data owner support.In this paper,we propose an encrypted data searching scheme that can support multiple keywords fuzzy search with order preserving(PMS).First,a new spelling correction algorithm-(Possibility-Levenshtein based Spelling Correction)is proposed to correct user input errors,so that fuzzy keywords input can be supported.Second,Paillier encryption is introduced to calculate encrypted relevance score of multiple keywords for order preserving.Then,a queue-based query method is also applied in this scheme to break the linkability between the query keywords and search results and protect the access pattern.Our proposed scheme achieves fuzzy matching without expanding the index table or sacrificing computational efficiency.The theoretical analysis and experiment results show that our scheme is secure,accurate,error-tolerant and very efficient.
基金This research was supported in part by the Nature Science Foundation of China(Nos.62262033,61962029,61762055,62062045 and 62362042)the Jiangxi Provincial Natural Science Foundation of China(Nos.20224BAB202012,20202ACBL202005 and 20202BAB212006)+3 种基金the Science and Technology Research Project of Jiangxi Education Department(Nos.GJJ211815,GJJ2201914 and GJJ201832)the Hubei Natural Science Foundation Innovation and Development Joint Fund Project(No.2022CFD101)Xiangyang High-Tech Key Science and Technology Plan Project(No.2022ABH006848)Hubei Superior and Distinctive Discipline Group of“New Energy Vehicle and Smart Transportation”,the Project of Zhejiang Institute of Mechanical&Electrical Engineering,and the Jiangxi Provincial Social Science Foundation of China(No.23GL52D).
文摘In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.
基金supported by the Foundational Research Funds for the Central University(No.30918012204).
文摘Attribute-based encryption is cryptographic techniques that provide flexible data access control to encrypted data content in cloud storage.Each trusted authority needs proper management and distribution of secret keys to the user’s to only authorized user’s attributes.However existing schemes cannot be applied multiple authority that supports only a single keywords search compare to multi keywords search high computational burden or inefficient attribute’s revocation.In this paper,a ciphertext policy attribute-based encryption(CP-ABE)scheme has been proposed which focuses on multi-keyword search and attribute revocation by new policy updating feathers under multiple authorities and central authority.The data owner encrypts the keywords index under the initial access policy.Moreover,this paper addresses further issues such as data access,search policy,and confidentiality against unauthorized users.Finally,we provide the correctness analysis,performance analysis and security proof for chosen keywords attack and search trapdoor in general group model using DBDH and DLIN assumption.
文摘With the rapid growth of spatial data,POI(Point of Interest)is becoming ever more intensive,and the text description of each spatial point is also gradually increasing.The traditional query method can only address the problem that the text description is less and single keyword query.In view of this situation,the paper proposes an approximate matching algorithm to support spatial multi-keyword.The fuzzy matching algorithm is integrated into this algorithm,which not only supports multiple POI queries,but also supports fault tolerance of the query keywords.The simulation results demonstrate that the proposed algorithm can improve the accuracy and efficiency of query.
基金supports in part by the National Key R&D Program of China(No.2020YFA0712300)in part by the National Natural Science Foundation of China(Grant Nos.62132005 and 62172162).
文摘Data outsourcing has become an industry trend with the popularity of cloud computing.How to search data securely and efficiently has received unprecedented attention.Dynamic Searchable Symmetric Encryption(DSSE)is an effective method to solve this problem,which supports file updates and keyword-based searches over encrypted data.Unfortunately,most existing DSSE schemes have privacy leakages during the addition and deletion phases,thus proposing the concepts of forward and backward privacy.At present,some secure DSSE schemes with forward and backward privacy have been proposed,but most of these DSSE schemes only achieve single-keyword query in the single-client setting,which seriously limits the application in practice.To solve this problem,we propose a multi-client and multikeyword searchable symmetric encryption scheme with forward and backward privacy(MMKFB).Our scheme focuses on the multi-keyword threshold queries in the multi-client setting,which is a new pattern of multi-keyword search realized with the help of additive homomorphism.And performance analysis and experiments demonstrate that our scheme is more practical for use in small and medium size databases.Especially when a large number of files are updated at once,our scheme has advantages over some existing DSSE schemes in terms of computational efficiency and client storage overhead.
基金supported by the National Natural Science Foundation of China(62272076)the Chongqing Natural Science Foundation of China(cstc2020jcyj-msxm X0343,cstc2020jcyj-msxm X1021)+1 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K20200602)the Sichuan Science and technology Foundation of China(22ZDYF3568)。
文摘To achieve the confidentiality and retrievability of outsourced data simultaneously,a dynamic multi-keyword fuzzy ranked search scheme(DMFRS)with leakage resilience over encrypted cloud data based on two-level index structure was proposed.The first level index adopts inverted index and orthogonal list,combined with 2-gram and location-sensitive Hashing(LSH)to realize a fuzzy match.The second level index achieves user search permission decision and search result ranking by combining coordinate matching with term frequency-inverse document frequency(TF-IDF).A verification token is generated within the results to verify the search results,which prevents the potential malicious tampering by cloud service providers(CSP).The semantic security of DMFRS is proved by the defined leakage function,and the performance is evaluated based on simulation experiments.The analysis results demonstrate that DMFRS gains certain advantages in security and performance against similar schemes,and it meets the needs of storage and privacy-preserving for outsourcing sensitive data.
