scholarly journals Protected and Flexible Fulti-Keyword Search Model Over Encoded Cloud Data

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2075-2079
Keyword(s):  
Large Scale ◽  
Keyword Search ◽  
Search Algorithm ◽  
Modern Technology ◽  
Index Structure ◽  
Inner Product ◽  
Cloud Data ◽  
Dynamic Update ◽  
And Performance ◽  
Cryptographic Techniques

Cloud computing is modern technology as a new computing model in number of business domains. Large numbers of large scale departments are starting to shift the data on to the cloud environment. With the benefit of storage as a service many enterprises are moving their valuable data to the cloud, since it costs less, easily scalable and can be accessed from anywhere any time. Improved dynamic multi-keyword ranking search scheme with top key via encrypted cloud data that simultaneously supports dynamic update operations as deleting and inserting documents. Greedy depth first search algorithm is provided for efficiency multi keywords on place and index structure. Cryptography is one of the establishing trust models. Searchable security is a cryptographic method to provide security. In number of researchers have been working on developing privacy and efficient searchable encryptiontypes. We take new effective cryptographic techniques based on data structures like CRSA and B-Tree to enhance the level of privacy. We propose new multi-keyword search query over encrypted cloud information in retrieving top k scored documents. The vector space model and TFIDF model are used to build index and query generation. This paper focuses on multi keyword search based on ranking over an encrypted cloud data. The search uses the feature of similarity and inner product similarity matching. We propose to support the top-k Multi-full-text search for security and performance analysis show that the proposed model guarantees a high safety and practicality and dynamic update operations, such as deleting and adding documents. The experimental results show that the overhead in computation and communication is low.

scholarly journals Enhanced Security for Dynamic Multi Keyword Ranked Search Using Greedy Best First Search

2019 ◽  
pp. 74-78
Author(s):  
Bibin Baby ◽  
Sharmila Banu
Keyword(s):  
Keyword Search ◽  
Search Algorithm ◽  
Security And Privacy ◽  
Index Structure ◽  
Sensitive Data ◽  
Cloud Data ◽  
Encryption Schemes ◽  
Query Generation ◽  
Ranked Search ◽  
Best First Search

Today, due to the enormous growth of data technology in cloud computing, the data owners are stimulated to outsource their data in data management to reduce cost and for the convenient. Data confidentiality, in general, can be obtained by encrypting the data before it is outsourced. The client stores the encrypted data to the cloud using Searchable encryption schemes and applies keyword search techniques over cipher text domain. But the main problem in outsourcing is the lack of security and privacy for the sensitive data. So, to overcome this, for privacy requirement, the sensitive data can be encrypted before it is outsourced. Various methods were proposed to preserve the privacy and to provide security to the cloud data which are encrypted. Here in this paper, we proposed a tree-based search method over the encrypted datain the cloud that supports dynamic operation and multi-keyword ranked search. Clearly, the commonly used “inverse document frequency (IDF) term frequency (TF)” model and the vector space method are joined in the query generation and index creation to give multi-keyword ranked search. To get high search efficiency, a tree-type index structure, “Greedy Best-first Search” algorithm is proposed based on the tree- index.

scholarly journals MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Zhu Xiangyang ◽  
Dai Hua ◽  
Yi Xun ◽  
Yang Geng ◽  
Li Xiao
Keyword(s):  
Data Storage ◽  
Search Algorithm ◽  
Privacy Preserving ◽  
Index Structure ◽  
Inner Product ◽  
Agglomerative Clustering ◽  
Text Search ◽  
Cloud Data ◽  
Hardware Failure ◽  
Hierarchical Agglomerative Clustering

With the development of cloud computing, services outsourcing in clouds has become a popular business model. However, due to the fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is exposed, which could bring serious privacy disclosure. In addition, some unexpected events, such as software bugs and hardware failure, could cause incomplete or incorrect results returned from clouds. In this paper, we propose an efficient and accurate verifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering, which is named MUSE. In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which is based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters. Based on the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and thus accelerate the search process. The secure inner product algorithm is used to encrypted the HAC-tree index and the query vector. Meanwhile, a completeness verification algorithm is given to verify search results. Experiment results demonstrate that the proposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively.

scholarly journals Adaptive Secure Cross-Cloud Data Collaboration with Identity-Based Cryptography and Conditional Proxy Re-Encryption

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qinlong Huang ◽  
Yue He ◽  
Wei Yue ◽  
Yixian Yang
Keyword(s):  
Service Providers ◽  
Cloud Service ◽  
Security And Privacy ◽  
Broadcast Encryption ◽  
Cloud Data ◽  
Identity Based ◽  
Identity Based Cryptography ◽  
And Performance ◽  
Cryptographic Techniques ◽  
Data Collaboration

Data collaboration in cloud computing is more and more popular nowadays, and proxy deployment schemes are employed to realize cross-cloud data collaboration. However, data security and privacy are the most serious issues that would raise great concerns from users when they adopt cloud systems to handle data collaboration. Different cryptographic techniques are deployed in different cloud service providers, which makes cross-cloud data collaboration to be a deeper challenge. In this paper, we propose an adaptive secure cross-cloud data collaboration scheme with identity-based cryptography (IBC) and proxy re-encryption (PRE) techniques. We first present a secure cross-cloud data collaboration framework, which protects data confidentiality with IBC technique and transfers the collaborated data in an encrypted form by deploying a proxy close to the clouds. We then provide an adaptive conditional PRE protocol with the designed full identity-based broadcast conditional PRE algorithm, which can achieve flexible and conditional data re-encryption among ciphertexts encrypted in identity-based encryption manner and ciphertexts encrypted in identity-based broadcast encryption manner. The extensive analysis and experimental evaluations demonstrate the well security and performance of our scheme, which meets the secure data collaboration requirements in cross-cloud scenarios.

Enhancing the Security and Performance of Cloud for E-Governance Infrastructure

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Large Scale ◽  
Governance System ◽  
Readiness Assessment ◽  
Cloud Data ◽  
Governance Model ◽  
Proposed Model ◽  
And Performance ◽  
Governance Infrastructure ◽  
Hashing Function ◽  
Distributed Infrastructure

E-Governance is getting momentous in India. Over the years, e-Governance has played a major part in every sphere of the economy. In this paper, we have proposed E-MODI (E-governance model for open distributed infrastructure) a centralized e-Governance system for government of India, the implementation of this system is technically based on open distributed infrastructure which comprises of various government bodies in one single centralized unit. Our proposed model identifies three different patterns of cloud computing which are DGC, SGC and CGC. In addition, readiness assessment of the services needs to migrate into cloud. In this paper, we propose energy efficient VM allocation algorithm to achieve higher energy efficiency in large scale cloud data centers when system on optimum mode. Our objectives have been explained in details and experiments were designed to demonstrate the robustness of the multi-layered security which is an integration of High secure lightweight block cipher CSL along with Ultra powerful BLAKE3 hashing function in order to maintain information security triad.

Secure and Privacy Preserving Keyword Search over the Large Scale Cloud Data

2016 ◽  
pp. 200-215
Author(s):  
Wei Zhang ◽  
Jie Wu ◽  
Yaping Lin
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Keyword Search ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Data Utilization ◽  
Cloud Data ◽  
Efficient Resource ◽  
Efficient Data

Cloud computing has attracted a lot of interests from both the academics and the industries, since it provides efficient resource management, economical cost, and fast deployment. However, concerns on security and privacy become the main obstacle for the large scale application of cloud computing. Encryption would be an alternative way to relief the concern. However, data encryption makes efficient data utilization a challenging problem. To address this problem, secure and privacy preserving keyword search over large scale cloud data is proposed and widely developed. In this paper, we make a thorough survey on the secure and privacy preserving keyword search over large scale cloud data. We investigate existing research arts category by category, where the category is classified according to the search functionality. In each category, we first elaborate on the key idea of existing research works, then we conclude some open and interesting problems.

Study of Dynamic Multikeyword Text Search Techniques over Encrypted Data in Cloud

2017 ◽  
Vol 7 (8) ◽  
pp. 326
Author(s):  
Ankita Puri ◽  
Naveen Kumari
Keyword(s):  
Data Retrieval ◽  
Modern Technology ◽  
Inner Product ◽  
Data Outsourcing ◽  
Keyword Query ◽  
Cloud Data ◽  
Data Services ◽  
Cloud Server ◽  
Efficient Data ◽  
Ranked Search

Day by Day ,with the advancement of modern technology over cloud computing motivating the data owners to outsource their data to the cloud server like Amazon, Microsoft, Azure etc .With the help of data outsourcing ,the organization can provide reliable data services to their user without any management of the overhead concern. Suppose, a large number of users that are on cloud and large number of documents on cloud, Its important for the service provider to allow multi-keyword query and provided the result that meet efficient data retrieval needs. In this paper, for the first time, we define and solve the challenging problem of privacy preserving multi-keyword ranked search over encrypted cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Among various multi-keyword semantics, we choose the efficient principle of “coordinate matching”, i.e., as many matches as possible, to capture the similarity between search query and data documents, and further use “inner product similarity” to quantitatively formalize such principle for similarity measurement.

Efficient fuzzy keyword search scheme over encrypted data in cloud computing based on Bed-tree index structure

2021 ◽  
pp. 1-13
Author(s):  
Dongping Hu ◽  
Aihua Yin
Keyword(s):  
Cloud Computing ◽  
Keyword Search ◽  
Hamming Distance ◽  
Security Analysis ◽  
Index Structure ◽  
Search Performance ◽  
Encrypted Data ◽  
Fuzzy Keyword Search ◽  
Work Security ◽  
And Performance

In cloud computing, enabling search directly over encrypted data is an important technique to effectively utilize encrypted data. Most of the existing techniques are focusing on fuzzy keyword search as it helps achieve more robust search performance by tolerating misspelling or typos of data users. Existing works always build index without classifying keywords in advance. They suffer from efficiency issue. Furthermore, Euclidean distance or Hamming distance is often chosen to evaluate strings’ similarity, ignoring prefixes matching and the influence of strings’ length on the accuracy. We propose an efficient fuzzy keyword search scheme with lower computation cost and higher accuracy to address the aforementioned problems. We employ the sub-dictionaries technique and the Bed-tree structure to build an index with three layers for achieving better search efficiency. With this index structure, the server could locate the keyword and could narrow the search scope quickly. The Jaro-Winkler distance is introduced to qualify the strings’ similarity by considering the prefixes matching and string length. The secure privacy mechanism is incorporated into the design of our work. Security analysis and performance evaluation demonstrate our scheme is more efficient compared to the existing one while guaranteeing security.

scholarly journals Conjunctive and Disjunctive Keyword Search over Encrypted Mobile Cloud Data in Public Key System

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Zhang ◽  
Yin Li ◽  
Yifan Wang
Keyword(s):  
Vector Space ◽  
Keyword Search ◽  
Vector Space Model ◽  
Public Key ◽  
Inner Product ◽  
Mobile Cloud ◽  
Encryption Scheme ◽  
Public Key Encryption ◽  
Cloud Data ◽  
Space Model

The searchable encryption scheme can perform keywords search operation directly over encrypted data without decryption, which is crucial to cloud storage, and has attracted a lot of attention in these years. However, it is still an open problem to develop an efficient public key encryption scheme supporting conjunctive and a disjunctive keyword search simultaneously. To achieve this goal, we introduce a keyword conversion method that can transform the query and index keywords into a vector space model. Through applying a vector space model to a predicate encryption scheme supporting inner product, we propose a novel public key encryption scheme with conjunctive and disjunctive keyword search. The experiment result demonstrates that our scheme is more efficient in both time and space as well as more suitable for the mobile cloud compared with the state-of-art schemes.

scholarly journals Study of Dynamic Multikeyword Text Search Techniques over Encrypted Data in Cloud

2017 ◽  
Vol 7 (7) ◽  
pp. 313
Author(s):  
Ankita Puri ◽  
Naveen Kumari
Keyword(s):  
Data Retrieval ◽  
Modern Technology ◽  
Inner Product ◽  
Data Outsourcing ◽  
Keyword Query ◽  
Cloud Data ◽  
Data Services ◽  
Cloud Server ◽  
Efficient Data ◽  
Ranked Search

Day by Day ,with the advancement of modern technology over cloud computing motivating the data owners to outsource their data to the cloud server like Amazon, Microsoft, Azure etc .With the help of data outsourcing ,the organization can provide reliable data services to their user without any management of the overhead concern. Suppose, a large number of users that are on cloud and large number of documents on cloud, Its important for the service provider to allow multi-keyword query and provided the result that meet efficient data retrieval needs. In this paper, for the first time, we define and solve the challenging problem of privacy preserving multi-keyword ranked search over encrypted cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Among various multi-keyword semantics, we choose the efficient principle of “coordinate matching”, i.e., as many matches as possible, to capture the similarity between search query and data documents, and further use “inner product similarity” to quantitatively formalize such principle for similarity measurement.

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