Verifiable keyword search for secure big data-based mobile healthcare networks with fine-grained authorization control

2018 ◽  
Vol 87 ◽  
pp. 712-724 ◽  
Author(s):  
Zehong Chen ◽  
Fangguo Zhang ◽  
Peng Zhang ◽  
Joseph K. Liu ◽  
Jiwu Huang ◽  
...  
Keyword(s):  
Big Data ◽  
Keyword Search ◽  
Fine Grained ◽  
Mobile Healthcare ◽  
Authorization Control

scholarly journals Fine-Grained Ranked Multi-Keyword Search Over Hierarchical Data for IoT-Oriented Health System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 101969-101980 ◽  
Author(s):  
Jianfei Sun ◽  
Shengnan Hu ◽  
Xuyun Nie
Keyword(s):  
Health System ◽  
Keyword Search ◽  
Hierarchical Data ◽  
Fine Grained

scholarly journals A Lightweight Fine-Grained Search Scheme over Encrypted Data in Cloud-Assisted Wireless Body Area Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mingsheng Cao ◽  
Luhan Wang ◽  
Zhiguang Qin ◽  
Chunwei Lou
Keyword(s):  
Keyword Search ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Security And Privacy ◽  
Body Area ◽  
Encrypted Data ◽  
Fine Grained ◽  
Resource Limited ◽  
Attribute Based Encryption ◽  
Ciphertext Policy

The wireless body area networks (WBANs) have emerged as a highly promising technology that allows patients’ demographics to be collected by tiny wearable and implantable sensors. These data can be used to analyze and diagnose to improve the healthcare quality of patients. However, security and privacy preserving of the collected data is a major challenge on resource-limited WBANs devices and the urgent need for fine-grained search and lightweight access. To resolve these issues, in this paper, we propose a lightweight fine-grained search over encrypted data in WBANs by employing ciphertext policy attribute based encryption and searchable encryption technologies, of which the proposed scheme can provide resource-constraint end users with fine-grained keyword search and lightweight access simultaneously. We also formally define its security and prove that it is secure against both chosen plaintext attack and chosen keyword attack. Finally, we make a performance evaluation to demonstrate that our scheme is much more efficient and practical than the other related schemes, which makes the scheme more suitable for the real-world applications.

Practical Attribute-Based Conjunctive Keyword Search Scheme

2019 ◽  
Vol 63 (8) ◽  
pp. 1203-1215 ◽  
Author(s):  
Yang Chen ◽  
Wenmin Li ◽  
Fei Gao ◽  
Kaitai Liang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Keyword Search ◽  
Inner Product ◽  
Real World Data ◽  
Data Set ◽  
Fine Grained ◽  
Keyword Guessing Attack ◽  
Secure Search ◽  
Data Owner ◽  
Cryptographic Primitive ◽  
Guessing Attack

Abstract To date cloud computing may provide considerable storage and computational power for cloud-based applications to support cryptographic operations. Due to this benefit, attribute-based keyword search (ABKS) is able to be implemented in cloud context in order to protect the search privacy of data owner/user. ABKS is a cryptographic primitive that can provide secure search services for users but also realize fine-grained access control over data. However, there have been two potential problems that prevent the scalability of ABKS applications. First of all, most of the existing ABKS schemes suffer from the outside keyword guessing attack (KGA). Second, match privacy should be considered while supporting multi-keyword search. In this paper, we design an efficient method to combine the keyword search process in ABKS with inner product encryption and deploy several proposed techniques to ensure the flexibility of retrieval mode, the security and efficiency of our scheme. We later put forward an attribute-based conjunctive keyword search scheme against outside KGA to solve the aforementioned problems. We provide security notions for two types of adversaries and our construction is proved secure against chosen keyword attack and outside KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the results of the simulation show that our scheme achieves stronger security without yielding significant cost of storage and computation.

A Fine‐Grained Distribution Approach for ETL Processes in Big Data Environments

2017 ◽  
Vol 111 ◽  
pp. 114-136 ◽  
Author(s):  
Mahfoud Bala ◽  
Omar Boussaid ◽  
Zaia Alimazighi
Keyword(s):  
Big Data ◽  
Fine Grained

scholarly journals Improving the data access control using blockchain for healthcare domain

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 901
Author(s):  
Olaosebikan Tahir Yinka ◽  
Su-Cheng Haw ◽  
Timothy Tzen Vun Yap ◽  
Samini Subramaniam
Keyword(s):  
Big Data ◽  
Identity Management ◽  
Performance Metrics ◽  
Data Access ◽  
Data Access Control ◽  
Proposed Model ◽  
Big Data Technologies ◽  
Access To Data ◽  
Authorization Control ◽  
Privacy Issues

Introduction: Unauthorized access to data is one of the most significant privacy issues that hinder most industries from adopting big data technologies. Even though specific processes and structures have been put in place to deal with access authorization and identity management for large databases nonetheless, the scalability criteria are far beyond the capabilities of traditional databases. Hence, most researchers are looking into other solutions, such as big data management. Methods: In this paper, we firstly study the strengths and weaknesses of implementing cryptography and blockchain for identity management and authorization control in big data, focusing on the healthcare domain. Subsequently, we propose a decentralized data access and sharing system that preserves privacy to ensure adequate data access management under the blockchain. In addition, we designed a blockchain framework to resolve the decentralized data access and sharing system privacy issues, by implementing a public key infrastructure model, which utilizes a signature cryptography algorithm (elliptic curve and signcryption). Lastly, we compared the proposed blockchain model to previous techniques to see how well it performed. Results: We evaluated the blockchain on four performance metrics which include throughput, latency, scalability, and security. The proposed blockchain model was tested using a sample of 5000 patients and 500,000 observations. The performance evaluation results further showed that the proposed model achieves higher throughput and lower latency compared to existing approaches when the workload varies up to 10,000 transactions. Discussion: This research reviews the importance of blockchains as they provide infinite possibilities to individuals, companies, and governments.

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.

Applying Big Data visualization to detect trends in 30 years of performance reports

Evaluation ◽  
2020 ◽  
Vol 26 (4) ◽  
pp. 516-540
Author(s):  
Eran Raveh ◽  
Yuval Ofek ◽  
Ron Bekkerman ◽  
Hertzel Cohen
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Science ◽  
Keyword Search ◽  
Mining Machine ◽  
Textual Information ◽  
Big Data Visualization ◽  
Performance Reports ◽  
User Friendly ◽  
Support Evaluation

Evaluators worldwide are dealing with a growing amount of unstructured electronic data, predominantly in textual format. Currently, evaluators analyze textual Big Data primarily using traditional content analysis methods based on keyword search, a practice that is limited to iterating over predefined concepts. But what if evaluators cannot define the necessary keywords for their analysis? Often we should examine trends in the way certain organizations have been operating, while our raw data are gigabytes of documents generated by that organization over decades. The problem is that in many cases we do not know what exactly we need to look for. In such cases, traditional analytical machinery would not provide an adequate solution within reasonable time—instead, heavy-lifting Big Data Science should be applied. We propose an automated, quantitative, user-friendly methodology based on text mining, machine learning, and data visualization, which assists researchers and evaluation practitioners to reveal trends, trajectories, and interrelations between bits and pieces of textual information in order to support evaluation. Our system automatically extracts a large amount of descriptive terminology for a particular domain in a given language, finds semantic connections between documents based on the extracted terminology, visualizes the entire document repository as a graph of semantic connections, and leads the user to the areas on that graph where most interesting trends can be observed. This article exemplifies the new method on 1700 performance reports, showing that the method can be used successfully, supplying evaluators with highly important information which cannot be revealed using other methods. Such exploratory exercise is vital as a preliminary exploratory phase for evaluations involving unstructured Big Data, after which a range of evaluation methods can be applied. We argue that our system can be successfully implemented on any domain evaluated.

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