Data Security Framework Research Articles

Blockchain Technologies in Healthcare System for Real Time Applications Using IoT and Deep Learning Techniques

Data transparency, flexible access, immutability, privacy, audit, traceability, data provenance, trust, and security are fundamental issues for modern healthcare data management systems. As a promising new technology, blockchain has the potential to enhance healthcare data management functions by boosting data efficiency and guaranteeing trust. The present research looked into the benefits of blockchain technology in healthcare and the challenges that have prevented its widespread implementation so far. Healthcare organisations around the world are using a variety of methods to modernise into more effective, coordinated and user-cantered structures. There is an increase in both human effort and security risks when dealing with massive amounts of data, such as reports and images for each individual. Internet of Things (IoT) solutions in healthcare aim to address these problems by enhancing patient care while reducing costs through more effective use of healthcare resources. However, many different types of intrusion can pose serious risks to IoT devices. In some cases, doctors will insist that their patients use only certain labs or pharmacies, regardless of the quality of the services they provide, simply to increase the doctor's bottom line. Because of this, protecting data is essential when discussing the Internet of Things. To solve these problems, Blockchain technology has emerged as the most reliable method for protecting the privacy of control systems in real time. In this paper, we will introduce a CNN-based healthcare data security framework using the blockchain technique by generating the hash of each data point, which will alert all users of the blockchain network to any unauthorised changes to data or breaches in the supply of medicines.

Blockchain-based AMI framework for data security and privacy protection

As a key component of smart grid (SG), advanced metering infrastructure (AMI) realizes two-way communication between users and power companies. Considering that AMI involves a large number of important data, which are related to the measurement of users’ electricity bills and the decision-making of power suppliers. Therefore, it is necessary to strengthen the protection of AMI data security and privacy to avoid economic losses caused by data leakage and tampering. Aiming at the problems of trust and single point of failure in the existing protection schemes, this paper proposes an AMI security framework based on blockchain. By combining the characteristics of blockchain such as decentration, tamper proof, traceability, the architecture of AMI data management system is reshaped. The technologies of peer-to-peer (P2P) transmission, asymmetric encryption, consensus algorithm and smart contract of blockchain are used to solve the problems of secure storage, transmission and application of AMI data. Through theoretical analysis and experiments, the feasibility of introducing blockchain technology into AMI is verified. The results show that the proposed framework is secure and reliable, which provides a strong guarantee for AMI data security and privacy protection.

A Framework for Data Security and Sharing in Cloud Computing Environments by Using Cryptographic Algorithms

Cloud storage is a liberating cloud application group to establish an internal database system. However, cloud storage raises security concerns. Inside the case of data sharing, the data is both cloud-specific and ancient music. Secure data distribution within a group which prevents the examiner from fearing legitimate but cruel agents important research questions. Data sharing and cloud storage play an important role in communication, as they can provide users with efficient and effective security services. To protect shared secret data, cryptographic methods are often applied. A large number of researchers, universities, government departments and commercial companies are adopting the cloud environment due to the initial investment, its high scalability and its many features. Despite many initiatives supporting the cloud environment, it faces many challenges. Data protection is a major concern in the information security and cloud computing industry. Many solutions have been developed to overcome this challenge. However, there is a there is not enough research between the current solutions and the emerging need to analyze, organize and analyze the important work that is done to study these solutions to respond to the field. This article provides an overview of research and process analysis, as well as an in-depth analysis of the topic of distributed security and data storage in cloud environments. Discussions on each dedicated process include: data protection services, rights and changes in the field, basic and comprehensive information including scope of work, achievements, limitations, future directions, etc. in any solution. In addition, a comprehensive and comparative analysis of these current methods is discussed. Then, the speed of the process is measured by the requirements of the search space and the future directions in the field are shown. The authors agree with this report. These plans will be what motivates researchers to do research in the field.

A novel security framework for healthcare data through IOT sensors

The Internet of Things (IoT) has played a crucial role in the distribution of health records and poses security issues to the patient-specific health information needed for remote hospital attention. The majority of publicly accessible security mechanisms for health information do not concentrate on the flow of information from IoT different sensors installed upon the person's blood through networking devices to primary health care centers. In this paper, we investigated the potential risks of unprotected transmission data, particularly among IoT sensor systems and network gateways. The study encourages the transmission of health insurance data to hospitals remotely. The proposed health care information model would encode immediately so that the sensing element before even being transferred to cryptographic techniques. To use a laboratory configuration with two-stage cryptography at the IoT sensor and two-stage decoding at the physician's surgery receptor, the prototype system was validated. The test results for a complete safety system for IoT - based on the transmission of healthcare data seem good. The study opens up new avenues for information security on IoT devices.

A Mathematical Queuing Model Analysis Using Secure Data Authentication Framework for Modern Healthcare Applications

Healthcare application is one of the most promising developments to provide on-time demand services to the end users, vehicles, and other Road Side Units (RSUs) in the urban environment. In recent years, several application interfaces have been developed to connect, communicate, and share the required services from one source to another. However, the urban environment holds a complex entity of both homogenous and heterogeneous devices to which the communication/sensing range between the devices leads to connectivity breakage, lack of needed service in time, and other environmental constraints. Also, security plays a vital role in allowing everyone in the urban area to access/request services according to their needs. Again, this leads to a massive breakthrough in providing reliable service to authentic users or a catastrophic failure of service denial involving unauthorized user access. This paper proposes a novel topological architecture, Secure Authentication Relay-based Urban Network (S-ARUN), designed for healthcare and other smart city applications for registered transportation stakeholders. The registered stakeholders hold a built-in data security framework with three subsystems connected to the S-ARUN topology: (1) authentication subsystem: the stakeholder must identify themselves to the source responder as part of the authentication subsystem before transmitting the actual data service request; (2) connectivity subsystem: to periodically check the connection state of stakeholders as they travel along with the road pattern; and (3) service subsystem: each source responder will keep a separate queue for collecting data service requests, processing them quickly, and sending the results to the appropriate stakeholder. The Kerberos authentication method is used in working with S-ARUN’s model to connect the stakeholders securely and legitimately. The performance of the proposed S-ARUN is assessed, and the performance metric toward key generation and other data security-related metrics is tested with existing schemes.

Privacy-aware genetic algorithm based data security framework for distributed cloud storage

An increased use of data driven applications and integrated systems have caused an accelerating expansion in data volumes and increase in the number of digital records, over the past few decades. Exponentially growing data volumes being processed by large-scale distributed data-intensive applications have placed an increasing pressure on the underlying storage services for timely and efficient storage and retrieval of the data. The use of cloud storage is among the best strategies to efficiently store growing volumes of data. However, outsourcing data to public cloud storage leads to the challenge of data confidentiality preservation. Data Confidentiality is among the top challenges associated with cloud storage which have contributed substantially as an inhibitor for cloud computing adoption all over the world and is considered a serious concern, especially in case of big data, where securing data in a timely and accurate manner is an arduous task. Our study aims to contribute in anti-cybercrime by protecting the confidentiality of sensitive growing data. We enunciate an optimized confidentially preserving framework on distributed cloud storage, that works for growing data with time-efficiency and minimum memory usage. Our framework uses a merger of Genetic Algorithm (GA), parallel data distribution, and privacy-aware selective encryption techniques. The experiments and comparative analysis depict that our proposed framework outperforms others under consideration, in terms of execution time, memory usage and network throughput respectively.

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Topics such as computational sources and cloud-based transmission and security of big data have turned out to be a major new domain of exploration due to the exponential evolution of cloud-based data and grid facilities. Various categories of cloud services have been utilized more and more widely across a variety of fields like military, army systems, medical databases, and more, in order to manage data storage and resource calculations. Attribute-based encipherment (ABE) is one of the more efficient algorithms that leads to better consignment and safety of information located within such cloud-based storage amenities. Many outmoded ABE practices are useful for smaller datasets to produce fixed-size cryptograms with restricted computational properties, in which their characteristics are measured as evidence and stagnant standards used to generate the key, encipherment, and decipherment means alike. To surmount the existing problems with such limited methods, in this work, a dynamic nonlinear poly randomized quantum hash system is applied to enhance the safety of cloud-based information. In the proposed work, users’ attributes are guaranteed with the help of a dynamic nonlinear poly randomized equation to initialize the chaotic key, encipherment, and decipherment. In this standard, structured and unstructured big data from clinical datasets are utilized as inputs. Real-time simulated outcomes demonstrate that the stated standard has superior exactness, achieving over 90% accuracy with respect to bit change and over 95% accuracy with respect to dynamic key generation, encipherment time, and decipherment time compared to existing models from the field and literature. Experimental results are demonstrated that the proposed cloud security standard has a good efficiency in terms of key generation, encoding, and decoding process than the conventional methods in a cloud computing environment.

A signature-based data security and authentication framework for internet of things applications

<span>Internet of things (IoT) is the next big revolution in modernized network technologies connecting a massive number of heterogeneous smart appliances and physical objects. Owing to these technologies' novelty, various issues are characterized by security concerns are the most prioritized issue. A review of existing security approaches highlights that they are very particular about the solution towards a specific attack and cannot resist any unknown attacker. Therefore, this manuscript presents a novel computational model that introduces a unique authentication process using a simplified encryption strategy. The simulated study outcome shows that the proposed system offers efficient security and efficient data transmission performance in the presence of an unknown adversary. Hence, the study outcome exhibits better effects than frequently used security solutions when implemented in a vulnerable IoT environment.</span>

Hybrid Multi-User Based Cloud Data Security for Medical Decision Learning Patterns

Machine learning plays a vital role in the real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also these cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers in order to store medical records or machine learning patterns for decision making. Due to high computational memory and time, these cloud systems require an efficient data security framework in order to provide strong data access control among the multiple users. In this paper, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases, data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering and security framework has better computational efficiency than the conventional approaches on large medical decision patterns.

A Novel Security Framework for Medical Data in IoT Ecosystems

The Internet of Things (IoT) is integrated with the healthcare system to create an intelligent hospital ecosystem. Protecting the privacy and security of medical data is a central problem in such an ecosystem. This article proposes a novel yet simple security framework to ensure the confidentiality of medical data during the communication between IoT hops. The proposed framework utilizes the concept of encryption to ensure the security. It essentially involves inducing confusion among pixels by employing image pixel scrambling achieved by spiralizing the medical data matrix, followed by a diffusion process using matrix exponentials and pseudorandom number generation by sequential chaotic maps. A reliable decryption scheme is finally designed to obtain the original data from the encrypted data. Experimental results and analyzes demonstrate the efficiency and robustness of the proposed framework and, thus, making it more suitable for the IoT ecosystem.

Data Security and Deduplication Framework for Securing and Deduplicating Users’ Data in Public and Private Cloud Environment

Maintaining the security of data stored in the public or private cloud is a more tedious task. The cloud is the only arrangement for storing enormous amounts of data, but there is a possibility of storing the same data more than once. The traditional security system generates different unreadable data for the same readable content of a file. Therefore, it is necessary to address data security of the cloud and duplication in cloud storage. This paper concentrates on developing a data security and deduplication framework with different security techniques and mechanisms to address the said difficulties in the cloud. The framework proposed in this paper focuses on reducing security vulnerability as well as data duplication. The paper describes the components used in the frameworks. The main research contribution of the framework is having enhanced the convergent encryption technique, key generation techniques, and deduplication mechanism for maintaining a single copy of data in the cloud. The proposed framework’s efficiency is measured by implementing the work by developing a cloud-based application that coded for all the procedures of the proposed framework and tested in the cloud environment.

Towards Big Data Security Framework by Leveraging Fragmentation and Blockchain Technology

The world is facing a growth in the amount and variety of data generated by both users and machines. Despite the exponential increases, the tools and technologies developed to manage these data volumes are not intended to meet security and data protection requirements. Additionally, most of the current big data security systems are offered by a centralized third party, which is vulnerable to many security threats. Blockchain technology plays a significant role by addressing modern technology concerns such as decentralization, non-tampering, trust, data ownership, and traceability, making it great potential to protect personal information. This research presents a new big data security solution empowered by blockchain technology and incorporates fragmentation, encryption, and access control techniques. Our proposed fragmentation algorithm takes into account the data owner’s demand for encryption to be added to the fragmentation process. Furthermore, data fragments will be stored in the distributed manner offered by the big data environment, resulting in an additional layer of data protection. In order to achieve an optimal security solution, we aim to enhance big data security with acceptable overhead and avoid the encryption overhead for non-sensitive and low-sensitive data portions. We present the results of our implemented techniques to highlight that the overheads (in terms of computation time) introduced by our solution are negligible relative to its security and privacy gains.

GA4GH Passport standard for digital identity and access permissions

SummaryThe Global Alliance for Genomics and Health (GA4GH) supports international standards that enable a federated data sharing model for the research community while respecting data security, ethical and regulatory frameworks, and data authorization and access processes for sensitive data. The GA4GH Passport standard (Passport) defines a machine-readable digital identity that conveys roles and data access permissions (called “visas”) for individual users. Visas are issued by data stewards, including data access committees (DACs) working with public databases, the entities responsible for the quality, integrity, and access arrangements for the datasets in the management of human biomedical data. Passports streamline management of data access rights across data systems by using visas that present a data user’s digital identity and permissions across organizations, tools, environments, and services. We describe real-world implementations of the GA4GH Passport standard in use cases from ELIXIR Europe, National Institutes of Health, and the Autism Sharing Initiative. These implementations demonstrate that the Passport standard has provided transparent mechanisms for establishing permissions and authorizing data access across platforms.

A cloud based 3-tier data security framework for industrial internet of things

<p>The convergence of microelectronics and micromechanical system within a sensing device, the proliferation of wireless communication, and the use of software-driven equipment are already changing the landscape of the manufacturing industries. This situation compels the industries to adopt industrial internet of things (IIoT) systems and processes with the main objective-to improve the processes and increase production. However, it is undeniable that despite IIoT advantages, it posed significant issues on the security and privacy on industry automation especially along data generation and control system. Hence, this paper proposes a cloud based 3-tier data security framework that performs two-level security verification processes. The framework has been tested and validated using an experimental industry automation system and has been found to be functionally effective with an acceptable handoff delay.</p>

A decentralized framework for device authentication and data security in the next generation internet of medical things

Internet of Things (IoT) applications have gained a huge momentum and have spanned across all domains adding innovations to the prevailing solutions. The IoT networks generate enormous data comprising meteorological information, patient critical body parameters, finance, logistics, location of a tracking object, etc. Security for sensitive data,such as patient body critical parameters from an attached monitoring device, streaming over IoT networks is vital and is a need of the hour. Data integrity and user (or device) authentication are crucial for building a trust-worthy communication network among the peers in IoT networks. Most networks still employ specific software encryption algorithms that provide considerable data security. But quantum computing has proved the vulnerability of computationally vigorous cryptographic algorithms. A decentralized and scalable framework for device authentication and data security is proposed in this paper based on blockchain platform and Physical Unclonable Functions (PUFs). An authentication protocol is developed using PUF-based cryptographic primitives. The PUF-based keys are hard to replicate and almost impossible to predict because of the randomness in the physical design and complex mathematical modeling of the system. Lightweight Smart contracts are used to facilitate role-based access control. Data privacy is preserved by storing the sensitive data off-chain. As a proof of concept, an IoT-based healthcare system based on Ethereum permissioned blockchain is developed using the proposed framework. The​ designed PUF exhibits 48.46% uniqueness and 2.38% reliability. A comparative analysis with existing similar models shows that the proposed approach is feasible and provides a scalable solution for device authentication and data security in resource-limited medical IoT networks.

A data security and integrity framework using third-party cloud auditing

A data security and integrity framework using third-party cloud auditing

RETRACTED ARTICLE: Machine Learning-Based Holistic Privacy Decentralized Framework for Big Data Security and Privacy in Smart City

RETRACTED ARTICLE: Machine Learning-Based Holistic Privacy Decentralized Framework for Big Data Security and Privacy in Smart City

Proposed Security Framework for Mobile Data Management System

Portable devices such as smartphones, tablet PCs, and PDAs are a useful combination of hardware and software turned toward the mobile workers. While they present the ability to review documents, communicate via electronic mail, appointments management, meetings, etc. They usually lack a variety of essential security features. To address the security concerns of sensitive data, many individuals and organizations, knowing the associated threats mitigate them through improving authentication of users, encryption of content, protection from malware, firewalls, intrusion prevention, etc. However, no standards have been developed yet to determine whether such mobile data management systems adequately provide the fundamental security functions demanded by organizations and whether these functions have been securely developed. Therefore, this paper proposes a security framework for mobile data that combines core security mechanisms to avoid these problems and protects sensitive information without spending time and money deploying several new applications.

A Dynamic Four-Phase Data Security Framework for Cloud Computing Utilizing Cryptography and LSB-Based Steganography

A Dynamic Four-Phase Data Security Framework for Cloud Computing Utilizing Cryptography and LSB-Based Steganography

A Data Security Framework for Cloud Computing Services

Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloud-based technologies, such as the Internet of Things. With increasing industry adoption and migration of traditional computing services to the cloud, one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies. This work proposes a Data Security Framework for cloud computing services (CCS) that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS. This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation. The paper presents the design details of the framework, which consists of three stages: identification of data security requirements, management of data security risks and evaluation of data security performance in CCS.