Novel Proposed Work for Empirical Word Searching in Cloud Environment

People's lives have become much more convenient as a result of the development of cloud storage. The third-party server has received a lot of data from many people and businesses for storage. Therefore, it is necessary to ensure that the user's data is protected from prying eyes. In the cloud environment, searchable encryption technology is used to protect user information when retrieving data. The versatility of the scheme is, however, constrained by the fact that the majority of them only offer single-keyword searches and do not permit file changes.A novel empirical multi-keyword search in the cloud environment technique is offered as a solution to these issues. Additionally, it prevents the involvement of a third party in the transaction between data holder and user and guarantees integrity. Our system achieves authenticity at the data storage stage by numbering the files, verifying that the user receives a complete ciphertext. Our technique outperforms previous analogous schemes in terms of security and performance and is resistant to inside keyword guessing attacks.The server cannot detect if the same set of keywords is being looked for by several queries because our system generates randomized search queries. Both the number of keywords in a search query and the number of keywords in an encrypted document can be hidden. Our searchable encryption method is effective and protected from the adaptive chosen keywords threat at the same time

AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT

Abstract: Please refer to full text to view abstrac

An extensive research survey on data integrity and deduplication towards privacy in cloud storage

Owing to the highly distributed nature of the cloud storage system, it is one of the challenging tasks to incorporate a higher degree of security towards the vulnerable data. Apart from various security concerns, data privacy is still one of the unsolved problems in this regards. The prime reason is that existing approaches of data privacy doesn't offer data integrity and secure data deduplication process at the same time, which is highly essential to ensure a higher degree of resistance against all form of dynamic threats over cloud and internet systems. Therefore, data integrity, as well as data deduplication is such associated phenomena which influence data privacy. Therefore, this manuscript discusses the explicit research contribution toward data integrity, data privacy, and data deduplication. The manuscript also contributes towards highlighting the potential open research issues followed by a discussion of the possible future direction of work towards addressing the existing problems

A survey of machine and deep learning methods for privacy protection in the Internet of things

Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks.This work is partially supported by the Generalitat de Catalunya under grant 2017 SGR 962 and the HORIZON-GPHOENIX (101070586) and HORIZON-EUVITAMIN-V (101093062) projects.Peer ReviewedPostprint (published version

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

Security architecture for Fog-To-Cloud continuum system

Nowadays, by increasing the number of connected devices to Internet rapidly, cloud computing cannot handle the real-time processing. Therefore, fog computing was emerged for providing data processing, filtering, aggregating, storing, network, and computing closer to the users. Fog computing provides real-time processing with lower latency than cloud. However, fog computing did not come to compete with cloud, it comes to complete the cloud. Therefore, a hierarchical Fog-to-Cloud (F2C) continuum system was introduced. The F2C system brings the collaboration between distributed fogs and centralized cloud. In F2C systems, one of the main challenges is security. Traditional cloud as security provider is not suitable for the F2C system due to be a single-point-of-failure; and even the increasing number of devices at the edge of the network brings scalability issues. Furthermore, traditional cloud security cannot be applied to the fog devices due to their lower computational power than cloud. On the other hand, considering fog nodes as security providers for the edge of the network brings Quality of Service (QoS) issues due to huge fog device’s computational power consumption by security algorithms. There are some security solutions for fog computing but they are not considering the hierarchical fog to cloud characteristics that can cause a no-secure collaboration between fog and cloud. In this thesis, the security considerations, attacks, challenges, requirements, and existing solutions are deeply analyzed and reviewed. And finally, a decoupled security architecture is proposed to provide the demanded security in hierarchical and distributed fashion with less impact on the QoS.Hoy en día, al aumentar rápidamente el número de dispositivos conectados a Internet, el cloud computing no puede gestionar el procesamiento en tiempo real. Por lo tanto, la informática de niebla surgió para proporcionar procesamiento de datos, filtrado, agregación, almacenamiento, red y computación más cercana a los usuarios. La computación nebulizada proporciona procesamiento en tiempo real con menor latencia que la nube. Sin embargo, la informática de niebla no llegó a competir con la nube, sino que viene a completar la nube. Por lo tanto, se introdujo un sistema continuo jerárquico de niebla a nube (F2C). El sistema F2C aporta la colaboración entre las nieblas distribuidas y la nube centralizada. En los sistemas F2C, uno de los principales retos es la seguridad. La nube tradicional como proveedor de seguridad no es adecuada para el sistema F2C debido a que se trata de un único punto de fallo; e incluso el creciente número de dispositivos en el borde de la red trae consigo problemas de escalabilidad. Además, la seguridad tradicional de la nube no se puede aplicar a los dispositivos de niebla debido a su menor poder computacional que la nube. Por otro lado, considerar los nodos de niebla como proveedores de seguridad para el borde de la red trae problemas de Calidad de Servicio (QoS) debido al enorme consumo de energía computacional del dispositivo de niebla por parte de los algoritmos de seguridad. Existen algunas soluciones de seguridad para la informática de niebla, pero no están considerando las características de niebla a nube jerárquica que pueden causar una colaboración insegura entre niebla y nube. En esta tesis, las consideraciones de seguridad, los ataques, los desafíos, los requisitos y las soluciones existentes se analizan y revisan en profundidad. Y finalmente, se propone una arquitectura de seguridad desacoplada para proporcionar la seguridad exigida de forma jerárquica y distribuida con menor impacto en la QoS.Postprint (published version

BMSQABSE: Design of a Bioinspired Model to Improve Security & QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications

Attribute-based searchable encryption (ABSE) is a sub-field of security models that allow intensive searching capabilities for cloud-based shared storage applications. ABSE Models require higher computational power, which limits their application to high-performance computing devices. Moreover, ABSE uses linear secret sharing scheme (LSSS), which requires larger storage when compared with traditional encryption models. To reduce computational complexity, and optimize storage cost, various researchers have proposed use of Machine Learning Models (MLMs), that assist in identification & removal of storage & computational redundancies. But most of these models use static reconfiguration, thus cannot be applied to large-scale deployments. To overcome this limitation, a novel combination of Grey Wolf Optimization (GWO) with Particle Swarm Optimization (PSO) model to improve Security & QoS performance for Blockchain-powered Attribute-based Searchable Encryption deployments is proposed in this text. The proposed model augments ABSE parameters to reduce its complexity and improve QoS performance under different real-time user request scenarios. It intelligently selects cyclic source groups with prime order & generator values to create bilinear maps that are used for ABSE operations. The PSO Model assists in generation of initial cyclic population, and verifies its security levels, QoS levels, and deployment costs under multiple real-time cloud scenarios. Based on this initial analysis, the GWO Model continuously tunes ABSE parameters in order to achieve better QoS & security performance levels via stochastic operations. The proposed BMSQABSE model was tested under different cloud configurations, and its performance was evaluated for healthcare deployments. Based on this evaluation, it was observed that the proposed model achieved 8.3% lower delay, with 4.9% lower energy consumption, 14.5% lower storage requirements when compared with standard ABSE models. It was able to mitigate Distributed Denial of Service (DDoS), Masquerading, Finney, and Sybil attacks, which assists in deploying the proposed model for QoS-aware highly secure deployments