Authorized Attribute-Based Encryption Multi-Keywords Search with Policy Updating

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.

Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

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.

Paillier-Based Fuzzy Multi-Keyword Searchable Encryption Scheme with Order-Preserving

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.

An Efficient Multi-Keyword Query Processing Strategy on P2P Based Web Search

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.

Research on Fuzzy Matching Query Algorithm Based on Spatial Multi-keyword

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.

3-Multi ranked encryption with enhanced security in cloud computing

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when requested.We refer to a model that satisfies all of the conditions a 3-multi ranked search model.However,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation center.That is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real life.In this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security assumptions.The proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same keyword.Moreover,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different key.Our evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

Security Model for Preserving Privacy over Encrypted Cloud Computing

In our today’s life, it is obvious that cloud computing is one of the new and most important innovations in the field of information technology which constitutes the ground for speeding up the development in great size storage of data as well as the processing and distribution of data on the largest scale. In other words, the most important interests of any data owner nowadays are related to all of the security as well as the privacy of data, especially in the case of outsourcing private data on a cloud server publicly which has not been one of the well-trusted and reliable domains. With the aim of avoiding any leakage or disclosure of information, we will encrypt any information important or confidential prior to being uploaded to the server and this may lead to an obstacle which encounters any attempt to support any efficient keyword query to be and ranked with matching results on such encrypted data. Recent researches conducted in this area have focused on a single keyword query with no proper ranking scheme in hand. In this paper, we will propose a new model called Secure Model for Preserving Privacy Over Encrypted Cloud Computing (SPEC) to improve the performance of cloud computing and to safeguard privacy of data in comparison to the results of previous researches in regard to accuracy, privacy, security, key generation, storage capacity as well as trapdoor, index generation, index encryption, index update, and finally files retrieval depending on access frequency.

Dynamic multi-keyword fuzzy ranked search with leakage resilience over encrypted cloud data

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.