scholarly journals An Efficient and Secure Multilevel Keyword Ranked Search for Encrypted Data

2020 ◽  
Vol 4 (4) ◽  
pp. 15-22
Author(s):  
Prasanth K Baby ◽  
Nikhil Samuel
Keyword(s):  
Data Privacy ◽  
Keyword Search ◽  
Search Time ◽  
Inner Product ◽  
Complex Data ◽  
Storage Space ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Efficient Retrieval ◽  
Ranked Search

with the growing digital communication and networks, the data owners are motivated to outsource their complex data to the global storage space. Greater flexibility and economic saving are the advantages of this global storage space. Before outsourcing the sensitive data, it has to be encrypted in order to enforce the data privacy. In the encrypted data, search service is important to get the necessary data. The stored data is relatively large so it requires multiple keywords in the search query and it will return document in the order of their relevance to these keywords searched. Related works on searchable encryption focus on single keyword search or Boolean keyword search and rarely sort the result and for the multi-keyword search coordinate matching, i.e., as many matches as possible, to effectively capture the relevance of outsourced documents to the query keywords and inner product similarity to evaluate such similarity measure. In Multi-keyword Ranked Search under the coordinate matching, the ranking helps for the efficient retrieval. The multilevel keyword ranked search is implemented by using the cache to reduce the search time.

scholarly journals Variable Techniques for Ciphertext Retrieval through Encrypted Data

2019 ◽  
Vol 8 (12) ◽  
pp. 3477-3479
Keyword(s):  
Cloud Storage ◽  
Data Privacy ◽  
Large Scale ◽  
Keyword Search ◽  
Maximum Size ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Data Utilization ◽  
Encrypted Data ◽  
Cloud Data

In recent years, Cloud computing provides strong grip and flexible access on outsource data, cloud storage, data privacy is major concern from to outsource their data, authenticated users are allowed to access this storage to prevent important and sensitive data. For data protection and utilization, we encrypt our sensitive data before outsourced our data because cannot trust storage server, are un-trusty but on other hand, data retrieval in encrypted format from cloud, is challenging task for data utilization, was encrypted from plaintext to ciphertext, when retrieves from cloud storage. However, searchable encryption schemes used Boolean search but they are unable to make data utilization for huge data and failed to handle multi-users access to retrieve ciphertext from cloud and user’s authentication. In this paper, we are using ranked keyword search over encrypted data by going k-documents at storage and using a Hierarchical Clustering Method is designed to guide more search semantics with an additional feature of making the system to cope the demand for fast ciphertext k-search in large scale environments explored the relevance score such as massive and big cloud data. This threshold splits the consequential clusters into sub-clusters until the necessity on the maximum size of cluster is reached. To make fetching search to be secure and privacy-preserving, it is built an index for searching on cloud data and retrieve the most relevant files from cloud. To defending privacy breaches from unauthorized users, users will go through authentication process and data retrieval time as well.

scholarly journals Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hua Dai ◽  
Hui Ren ◽  
Zhiye Chen ◽  
Geng Yang ◽  
Xun Yi
Keyword(s):  
Data Privacy ◽  
Logistic Map ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Service Recommendation ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Sorting Algorithms ◽  
Common Operation ◽  
Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

scholarly journals A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Run Xie ◽  
Chanlian He ◽  
Dongqing Xie ◽  
Chongzhi Gao ◽  
Xiaojun Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Privacy ◽  
Security Analysis ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Security Issues ◽  
Efficiency Comparison ◽  
Cloud Servers

With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

scholarly journals Efficient verifiable fuzzy keyword search over encrypted data in cloud computing

2013 ◽  
Vol 10 (2) ◽  
pp. 667-684 ◽  
Author(s):  
Jianfeng Wang ◽  
Hua Ma ◽  
Qiang Tang ◽  
Jin Li ◽  
Hui Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Keyword Search ◽  
Efficiency Analysis ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Proposed Model ◽  
Cloud Server ◽  
Encryption Schemes ◽  
User Data ◽  
Fuzzy Keyword Search

As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud by users. To maintain the confidentiality of sensitive user data against untrusted servers, the data should be encrypted before they are uploaded. However, this raises a new challenge for performing search over the encrypted data efficiently. Although the existing searchable encryption schemes allow a user to search the encrypted data with confidentiality, these solutions cannot support the verifiability of searching result. We argue that a cloud server may be selfish in order to save its computation ability or bandwidth. For example, it may execute only a fraction of the search and returns part of the searching result. In this paper, we propose a new verifiable fuzzy keyword search scheme based on the symbol-tree which not only supports the fuzzy keyword search, but also enjoys the verifiability of the searching result. Through rigorous security and efficiency analysis, we show that our proposed scheme is secure under the proposed model, while correctly and efficiently realizing the verifiable fuzzy keyword search. The extensive experimental results demonstrate the efficiency of the proposed scheme.

Privacy-Preserving Outsourced Similarity Search

2014 ◽  
Vol 25 (3) ◽  
pp. 48-71 ◽  
Author(s):  
Stepan Kozak ◽  
David Novak ◽  
Pavel Zezula
Keyword(s):  
Similarity Search ◽  
Data Privacy ◽  
System Analysis ◽  
Keyword Search ◽  
Evaluation Criteria ◽  
Similarity Index ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Scientific Data ◽  
Sensitive Data

The general trend in data management is to outsource data to 3rd party systems that would provide data retrieval as a service. This approach naturally brings privacy concerns about the (potentially sensitive) data. Recently, quite extensive research has been done on privacy-preserving outsourcing of traditional exact-match and keyword search. However, not much attention has been paid to outsourcing of similarity search, which is essential in content-based retrieval in current multimedia, sensor or scientific data. In this paper, the authors propose a scheme of outsourcing similarity search. They define evaluation criteria for these systems with an emphasis on usability, privacy and efficiency in real applications. These criteria can be used as a general guideline for a practical system analysis and we use them to survey and mutually compare existing approaches. As the main result, the authors propose a novel dynamic similarity index EM-Index that works for an arbitrary metric space and ensures data privacy and thus is suitable for search systems outsourced for example in a cloud environment. In comparison with other approaches, the index is fully dynamic (update operations are efficient) and its aim is to transfer as much load from clients to the server as possible.

scholarly journals Public Key Encryption with Keyword Search from Lattices in Multiuser Environments

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Daini Wu ◽  
Xiaoming Wang ◽  
Qingqing Gan
Keyword(s):  
Data Privacy ◽  
Keyword Search ◽  
Random Oracle Model ◽  
Random Oracle ◽  
Public Key ◽  
Public Key Encryption ◽  
Encrypted Data ◽  
Cloud Server ◽  
Learning With Errors ◽  
Multiuser Environments

A public key encryption scheme with keyword search capabilities is proposed using lattices for applications in multiuser environments. The proposed scheme enables a cloud server to check if any given encrypted data contains certain keywords specified by multiple users, but the server would not have knowledge of the keywords specified by the users or the contents of the encrypted data, which provides data privacy as well as privacy for user queries in multiuser environments. It can be proven secure under the standard learning with errors assumption in the random oracle model.

scholarly journals USING DIFFERENT SEARCHING SCHEMAS FOR FUZZY KEYWORD SEARCH OVER CLOUD DATA

2013 ◽  
pp. 41-44
Author(s):  
SYEDA FARHA SHAZMEEN ◽  
RANGARAJU DEEPIKA
Keyword(s):  
Cloud Computing ◽  
Data Privacy ◽  
Keyword Search ◽  
Text Search ◽  
Sensitive Data ◽  
Cloud Data ◽  
Fuzzy Search ◽  
Wild Card ◽  
Fuzzy Keyword Search ◽  
Search And Retrieval

Cloud Computing is a construct that allows you to access applications that actually reside at a location other than our computer or other internet-connected devices, Cloud computing uses internet and central remote servers to maintain data and applications, the data is stored in off-premises and accessing this data through keyword search. So there comes the importance of encrypted cloud data search Traditional keyword search was based on plaintext keyword search, but for protecting data privacy the sensitive data should be encrypted before outsourcing. Fuzzy keyword search greatly enhances system usability by returning the matching files; Fuzzy technique uses approximate full text search and retrieval. Three different Fuzzy Search Schemas, The wild card method, gram based method and tree traverse search scheme, are dicussed and also the efficiency of these algorithms is analyzed.

scholarly journals Privacy-Preserving and Efficient Public Key Encryption with Keyword Search Based on CP-ABE in Cloud

Cryptography ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 28
Author(s):  
Yunhong Zhou ◽  
Shihui Zheng ◽  
Licheng Wang
Keyword(s):  
Access Control ◽  
Data Privacy ◽  
Keyword Search ◽  
Good Method ◽  
Privacy Preserving ◽  
Public Key ◽  
Public Key Encryption ◽  
Encrypted Data ◽  
Fine Grained ◽  
Attribute Based Encryption

In the area of searchable encryption, public key encryption with keyword search (PEKS) has been a critically important and promising technique which provides secure search over encrypted data in cloud computing. PEKS can protect user data privacy without affecting the usage of the data stored in the untrusted cloud server environment. However, most of the existing PEKS schemes concentrate on data users’ rich search functionalities, regardless of their search permission. Attribute-based encryption technology is a good method to solve the security issues, which provides fine-grained access control to the encrypted data. In this paper, we propose a privacy-preserving and efficient public key encryption with keyword search scheme by using the ciphertext-policy attribute-based encryption (CP-ABE) technique to support both fine-grained access control and keyword search over encrypted data simultaneously. We formalize the security definition, and prove that our scheme achieves selective indistinguishability security against an adaptive chosen keyword attack. Finally, we present the performance analysis in terms of theoretical analysis and experimental analysis, and demonstrate the efficiency of our scheme.

Electronic Health Record Security in Cloud

2020 ◽  
pp. 22-47
Author(s):  
Desam Vamsi ◽  
Pradeep Reddy
Keyword(s):  
Data Privacy ◽  
Storage Systems ◽  
Homomorphic Encryption ◽  
Cost Effective ◽  
Healthcare Organizations ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Encrypted Data ◽  
Electronic Health ◽  
Somewhat Homomorphic Encryption

Security is the primary issue nowadays because cybercrimes are increasing. The organizations can store and maintain their data on their own, but it is not cost effective, so for convenience they are choosing cloud. Due to its popularity, the healthcare organizations are storing their sensitive data to cloud-based storage systems, that is, electronic health records (EHR). One of the most feasible methods for maintaining privacy is homomorphism encryption (HE). HE can combine different services without losing security or displaying sensitive data. HE is nothing but computations performed on encrypted data. According to the type of operations and limited number of operations performed on encrypted data, it is categorized into three types: partially homomorphic encryption (PHE), somewhat homomorphic encryption (SWHE), fully homomorphic encryption (FHE). HE method is very suitable for the EHR, which requires data privacy and security.

scholarly journals Logistic Regression on Homomorphic Encrypted Data at Scale

2019 ◽  
Vol 33 ◽  
pp. 9466-9471 ◽  
Author(s):  
Kyoohyung Han ◽  
Seungwan Hong ◽  
Jung Hee Cheon ◽  
Daejun Park
Keyword(s):  
Logistic Regression ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Training Data ◽  
Sensitive Data ◽  
Encrypted Data ◽  
The Public ◽  
Private Data ◽  
First Time ◽  
Practical Feasibility

Machine learning on (homomorphic) encrypted data is a cryptographic method for analyzing private and/or sensitive data while keeping privacy. In the training phase, it takes as input an encrypted training data and outputs an encrypted model without ever decrypting. In the prediction phase, it uses the encrypted model to predict results on new encrypted data. In each phase, no decryption key is needed, and thus the data privacy is ultimately guaranteed. It has many applications in various areas such as finance, education, genomics, and medical field that have sensitive private data. While several studies have been reported on the prediction phase, few studies have been conducted on the training phase.In this paper, we present an efficient algorithm for logistic regression on homomorphic encrypted data, and evaluate our algorithm on real financial data consisting of 422,108 samples over 200 features. Our experiment shows that an encrypted model with a sufficient Kolmogorov Smirnow statistic value can be obtained in ∼17 hours in a single machine. We also evaluate our algorithm on the public MNIST dataset, and it takes ∼2 hours to learn an encrypted model with 96.4% accuracy. Considering the inefficiency of homomorphic encryption, our result is encouraging and demonstrates the practical feasibility of the logistic regression training on large encrypted data, for the first time to the best of our knowledge.

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