Fine-grained data access control with attribute-hiding policy for cloud-based IoT

The final publication is available at Elsevier via https://doi.org/10.1016/j.comnet.2019.02.008. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Ciphertext-policy attribute-based encryption (CP-ABE) is a promising approach to achieve fine-grained access control over the outsourced data in Internet of Things (IoT). However, in the existing CP-ABE schemes, the access policy is either appended to the ciphertext explicitly or only partially hidden against public visibility, which results in privacy leakage of the underlying ciphertext and potential recipients. In this paper, we propose a fine-grained data access control scheme supporting expressive access policy with fully attribute hidden for cloud-based IoT. Specifically, the attribute information is fully hidden in access policy by using randomizable technique, and a fuzzy attribute positioning mechanism based on garbled Bloom filter is developed to help the authorized recipients locate their attributes efficiently and decrypt the ciphertext successfully. Security analysis and performance evaluation demonstrate that the proposed scheme achieves effective policy privacy preservation with low storage and computation overhead. As a result, no valuable attribute information in the access policy will be disclosed to the unauthorized recipients

Using Attribute-Based Access Control, Efficient Data Access in the Cloud with Authorized Search

The security and privacy issues regarding outsourcing data have risen significantly as cloud computing has grown in demand. Consequently, since data management has been delegated to an untrusted cloud server in the data outsourcing phase, data access control has been identified as a major problem in cloud storage systems. To overcome this problem, in this paper, the access control of cloud storage using an Attribute-Based Access Control (ABAC) approach is utilized. First, the data must be stored in the cloud and security must be strong for the user to access the data. This model takes into consideration some of the attributes of the cloud data stored in the authentication process that the database uses to maintain data around the recorded collections with the user\u27s saved keys. The clusters have registry message permission codes, usernames, and group names, each with its own set of benefits. In advance, the data should be encrypted and transferred to the service provider as it establishes that the data is still secure. But in some cases, the supplier\u27s security measures are disrupting. This result analysis the various parameters such as encryption time, decryption time, key generation time, and also time consumption. In cloud storage, the access control may verify the various existing method such as Ciphertext Policy Attribute-Based Encryption (CP-ABE) and Nth Truncated Ring Units (NTRU). The encryption time is 15% decreased by NTRU and 31% reduced by CP-ABE. The decryption time of the proposed method is 7.64% and 14% reduced by the existing method

A Novel Zero-Trust Network Access Control Scheme based on the Security Profile of Devices and Users

Security constitutes a principal concern for communication networks and services at present. This way, threats should be under control to minimize risks over time in real environments. With this aim, we introduce here a new approach for access control aimed to strengthen security in corporate networks and service providers related environments. Our proposal, named SADAC (Security Attribute-based Dynamic Access Control) presents three main novel features: (i) security related attributes regarding both configuration and operation are considered for network access control of final devices/users; (ii) a dynamic supervision procedure is implemented to evaluate the security profile associated to devices/users over time and, if so, to apply corresponding access restrictions; and (iii) a supervision procedure that also permits to diagnose the causes of inadequate security behaviours, so that the final devices/users can adapt their configuration and/or operation. We describe the overall access control methodology as well as the aspects for its implementation. In particular, we present and evaluate the specific deployment of SADAC for a corporate WiFi environment supported on a Raspberry Pi-based AP to provide Internet access to mobile devices. Through this experimentation we can conclude the convenience of adopting the approach for improving security by minimizing risks in network and communication environments

IoT Security Evolution: Challenges and Countermeasures Review

Internet of Things (IoT) architecture, technologies, applications and security have been recently addressed by a number of researchers. Basically, IoT adds internet connectivity to a system of intelligent devices, machines, objects and/or people. Devices are allowed to automatically collect and transmit data over the Internet, which exposes them to serious attacks and threats. This paper provides an intensive review of IoT evolution with primary focusing on security issues together with the proposed countermeasures. Thus, it outlines the IoT security challenges as a future roadmap of research for new researchers in this domain

An efficient filter with low memory usage for multimedia data of industrial Internet of Things

One of the essential concerns of Internet of Things (IoT) is in industrial systems or data architecture to support the evolutions in transportation and logistics. Considering the Industrial IoT (IIoT) openness, the need for accessibility, availability, and searching of data has rapidly increased. The primary purpose of this research is to propose an Efficient Two-Dimensional Filter (ETDF) to store multimedia data of IIoT applications in a specific format to achieve faster response and dynamic updating. This filter consists of a two-dimensional array and a hash function integrated into a cuckoo filter for efficient use of memory. This study evaluates the scalability of the filter by increasing the number of requests from 10,000 to 100,000. To assess the performance of the proposed filter, we measure the parameters of access time and lookup message latency. The results show that the proposed filter improves the access time by 12%, compared to a Fast Two-Dimensional Filter (FTDF). Moreover, it improves memory usage by 20% compared to FTDF. Experiments indicate a better access time of the proposed filter compared to other filters (i.e., Bloom, quotient, cuckoo, and FTD filters). Insertion and deletion times are essential parameters in comparing filters, so they are also analyzed

Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system

Marketing relacional y posicionamiento de la organización mega corredores de Seguros S.A.C., Trujillo – 2022

La investigación tuvo como objetivo determinar la relación entre marketing relacional y posicionamiento de la Organización Mega Corredores de Seguros S.A.C. - Trujillo 2022. La metodología fue de alcance correlacional y diseño no experimental, la muestra estuvo representada por 297 clientes a los que se les aplicó un cuestionario. Entre los principales hallazgos, se encontró una relación positiva moderada y significativa (Rho=0,324; p=0,000) entre la gestión de clientes y el posicionamiento; asimismo, se identificó una relación positiva moderada y significativa (Rho=0,543; p=0,000) entre la gestión de expectativas y el posicionamiento; también se encontró una relación positiva moderada y significativa (Rho=0,430; p=0,000) entre la gestión de lealtad y el posicionamiento. Se concluyó que existe una relación positiva moderada y significativa (Rho=0,533; p=0,000) entre el marketing relacional y el posicionamiento de la Organización Mega Corredores de Seguros S.A.C., refiriendo que si la empresa tiene interés en generar relaciones duraderas con su público objetivo, tiende a mejorar su posicionamiento en el mercado empresarial; además, este resultado permitió aceptar la hipótesis alterna y rechazar la hipótesis nul

Latency performance modelling in hyperledger fabric blockchain: Challenges and directions with an IoT perspective

Blockchain is a decentralized and distributed ledger technology that enables secure and transparent recording of transactions across multiple participants. Hyperledger Fabric (HLF), a permissioned blockchain, enhances performance through its modular design and pluggable consensus. However, integrating HLF with enterprise applications introduces latency challenges. Researchers have proposed numerous latency performance modelling techniques to address this issue. These studies contribute to a deeper understanding of HLF's latency by employing various modelling approaches and exploring techniques to improve network latency. However, existing HLF latency modelling studies lack an analysis of how these research efforts apply to specific use cases. This paper examines existing research on latency performance modelling in HLF and the challenges of applying these models to HLF-enabled Internet of Things (IoT) use cases. We propose a novel set of criteria for evaluating HLF latency performance modelling and highlight key HLF parameters that influence latency, aligning them with our evaluation criteria. We then classify existing papers based on their focus on latency modelling and the criteria they address. Additionally, we provide a comprehensive overview of latency performance modelling from various researchers, emphasizing the challenges in adapting these models to HLF-enabled IoT blockchain within the framework of our evaluation criteria. Finally, we suggest directions for future research and highlight open research questions for further exploration